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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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Initial experimental data set from the NETL ammonia combustion project being performed under the Advanced Turbines Multi-Year-Research-Plan. Release 1.0 includes fundamental measurements performed in the Pittsburgh Fundamental Combustion Laboratory between 2023-2024.
To study quantum properties of the hadron wavefunction at small x, we derived the reduced density matrix for soft gluons in the CGC framework. We explicitly showed that the reduced density matrix is not diagonal in the particle number basis. The off-diagonal components are usually ignored in the conventional parton model. We thus defined the density matrix of ignorance by keeping only the part of the reduced density matrix which can be probed in a limited set of experimental measurements. We calculated Von Neumann entropy for both the reduced and ignorance density matrices. The entropy of ignorance is always greater than the entanglement entropy (computed drom the reduced density matrix) of gluons. Finally, we showed that the CGC reduced density matrix for soft gluons can be diagonalized in a thermal basis with Boltzmann weights suggesting thermalization of new quasi-particle states which we dubbed entangolons.
As artificial intelligence, machine learning, and statistical modeling methods become commonplace in national security applications, the drive to create trusted analytics becomes increasingly important. The goal of this report is to identify areas of research that can provide the foundational understanding and technical prerequisites for the development and deployment of trusted analytics in national security settings. Our review of the literature covered several disjoint research communities, including computer science, statistics, human factors, and several branches of psychology and cognitive science, which tend not to interact with one another or cite each other's literatures. As a result, there exists no agreed-upon theoretical framework for understanding how various factors influence trust and no well-established empirical paradigm for studying these effects. This report therefore takes three steps. First, we define several key terms in an effort to provide a unifying language for trusted analytics and to manage the scope of the problem. Second, we outline an empirical perspective that identifies key independent, moderating, and dependent variables in assessing trusted analytics. Though not a substitute for a theoretical framework, the empirical perspective does support research and development of trusted analytics in the national security domain. Finally, we discuss several research gaps relevant to developing trusted analytics for the national security mission space.
The Plateau to River Groundwater Model (P2R Model) is a groundwater flow and contaminant fate and transport (F&T) simulation model used to support remedial activities conducted by CH2M HILL Plateau Remediation Company at the Hanford Site in Washington State. Figure 1-1 illustrates the P2R Model extents, discretization, and boundary conditions. The P2R Model is utilized in the Composite Analysis (CA) for the Hanford Site as the computational engine for computing F&T predictions as described in CP-60406, Hanford Site Composite Analysis Technical Approach Description: Groundwater. The model simulates contaminants of concern within the saturated zone of the uppermost aquifer beneath the Central Plateau and downgradient to the Columbia River. CP-57037, Model Package Report for the Plateau to River Model Version 8.3, documents the current version of the P2R Model including a description of the conceptual site model, model development and calibration, and limitations to the model application. The overall objective of the saturated zone modeling effort is to provide a basis for making informed remedial action decisions based on descriptions of current and expected future contaminant concentrations in groundwater at decision points within and downgradient of the Central Plateau of the Hanford Site. Specifically, the purpose of this environmental calculation is to describe the application of the hydraulic property fields and recharge parameters documented in ECF-HANFORD-20-0027, Null Space Monte Carlo Evaluation of the Plateau to River Model, to the CA flow and fate and transport simulation results to quantify the uncertainty in the simulated results due to input parameter selection. Use of numerical groundwater models is always accompanied with uncertainty in the results produced by a model because models are approximations of reality. Thus, by definition, models lack the detail to fully represent observed behavior. Use of numerical techniques, such as a NSMC analysis, can help in identifying and quantifying the potential uncertainties associated with a numerical model such as the P2R Model. The result of NSCM analysis is a set of F&T simulations that provide an estimate of the range of possible outcomes that are used to quantify the uncertainty in simulated concentrations produced using the base case simulations. The simulated concentrations from all simulations will support calculation of the uncertainty of the total dose calculated in a separate calculation.
Roundness (or circularity) and flatness measurements are made as part of the dimensional inspection process to certify that iridium alloy Clad Vent Set (CVS) cups meet the dimensional requirements of Iridium Alloy Cup Drawing M2D920101A005, Rev. 4 (see Figure 1). A Mahr Federal (Providence, RI) MarForm MMQ 400 Formtester is specified in the cup inspection Procedure GPHS-C-3624/25, Rev. 25 for roundness (dimension 16) and flatness (dimension 17). The Mahr Federal (Providence, RI) MarForm MMQ 400 Formtester stopped functioning in 2019. A new Mitutoyo RA-2200 Roundness Tester was purchased to replace the MMQ 400. Evaluations were conducted to qualify the new RA-2200 for CVS cup dimensional inspection work. The criterion for qualification was to ensure that the new RA-2200 instrument would yield roundness and flatness inspection results equivalent to or better than the predecessor MMQ 400 instrument.
Why a Set Topology? 1. Expand the array of tools given to physicists, educators, and students. 2. Through usages of PIC, MPM, and Monte Carlo. 3. My work at LANL was specified to handle requirements for the usage of MPM with FleCSI. 4. What we want it to do - phase change, simulating plasma, and other Multiphysics applications. 5. In FleCSI we already have mesh structures, we needed a way to represent those particles and certain behavior's of these particles that clients desire.
The purpose of this environmental calculation file (ECF) is to present the results of the exposure route-specific and total radiological dose assessments for the groundwater pathway based on the null space Monte Carlo (NSMC) groundwater concentrations as a part an uncertainty analysis for the updated Hanford Site Composite Analysis (CA). The Plateau-to River (P2R) Groundwater Model (CP-57037, Model Package Report: Plateau to River Groundwater Model Version 8.3) is the CA base case that simulates the fate and transport of radiological contaminants within the saturated zone of the uppermost aquifer beneath the Central Plateau and downgradient to the Columbia River. An NSMC analysis was performed to identify and quantify the potential uncertainties associated with the P2R Model. The result of the NSMC analysis is a set of flow and transport simulations that provide an estimated range of possible outcomes that are used to quantify the uncertainty associated with the simulated base case concentrations.
Windows cause approximately 1.7 quad of heating and cooling energy consumption in the United States. This energy consumption can be reduced by using high-efficiency window attachments. Common Venetian blinds and planar shades, such as roller shades, might block solar radiation, but they do not provide a significant improvement to window system thermal transmittance. Cellular shades have better thermal performance compared to other shading devices because of the honeycomb structure that traps air in pockets to create thermal insulation. However, evaluating the energy savings potential of cellular shades using experimental testing in commercial settings is limited. Moreover, the effect of cellular shades on daylighting and glare is yet to be evaluated using field testing. In this study, experimental testing of cellular shades was performed in a real building with emulated occupancy for both a cooling and a heating season. Compared with a room without shades, the use of cellular shades in experimental testing showed incremental energy savings of 4.6% to 9.4% for cooling and higher than 20% for heating. The experimental data were used to calibrate the baseline energy model and validate the cellular shades model. Annual simulation of cellular shades was performed for a medium office prototype building using the validated cellular shades model. The annual simulation was performed in Phoenix, Nashville, and Rochester. The annual savings for HVAC energy was 25% for Phoenix, 27% for Nashville, and 19% for Rochester.
This project brought DOE’s climate modeling effort with the Energy Exascale Earth System Model into the Polar Amplification Model Intercomparison Project (PAMIP), which is part of the sixth and latest Coupled Model Intercomparison Project, CMIP6. PAMIP examines the causes and consequences of polar amplification, when external forcing results in a larger temperature increase in high latitudes than the global average, in a coordinated set of model experiments with a common modeling protocol. Our teams from UC Irvine and the University of Toronto have designed, carried out, analyzed, and disseminated PAMIP output from the Energy Exascale Earth System Model (E3SM) and the Community Earth System Model (CESM). PAMIP’s ongoing stream of significant new results have advanced progress in the community’s understanding and led to new outstanding research questions that have motivated further work. PAMIP has led to improved consensus on the atmospheric response to sea ice loss. The important finding is that for a similar sea ice anomaly forcing, the simulated atmospheric response in the troposphere is remarkably consistent among the 16 models’ runs analyzed. The zonal-mean tropospheric response consists of a very robust equatorward shift of the westerly flow in mid-latitudes. However, while the multi-model mean response is robust, it has a weak amplitude relative to internal variability. We identified a weakness in the models (including E3SM) in terms of their response to sea-ice forcing that is related to eddy forcing (or nonlinear dynamical effects) at mid- to high latitudes. In fact, E3SM is an outlier in terms of the models participating, and in that sense, it turned out to be a vital participant model. We found that reductions in energy transport due sea-ice loss and involving dry air only are compensated by increases in moist energy transport from warmer sea surface temperature in midlatitudes. This leads to a large spread in energy transport into the Arctic and is a potential source of spread in Arctic amplification. We identified an important role that climate modes, including tropical modes of variability (El Nino and the Southern Oscillation (ENSO); the Quasi-biennial Oscillation (QBO)) play in the response to sea-ice anomalies, including in ocean coupled experiments. Similarly, we identified and quantified the contribution of sea-ice thickness to the atmospheric response compared to the response to sea-ice extent only. We found that it is important to run large ensembles and even with an ensemble size of 100 simulations the response is largely influenced by internal variability. We demonstrated convincingly that Ural blocking, not sea-ice loss, provides the weakening of the stratospheric polar vortex in fall/early winter and a negative phase of the North Atlantic Oscillation that can last for up to two months. However, sea-ice anomalies can influence the background flow so that the response to Ural blocking is more persistent under low sea-ice conditions in the Barents/Kara Sea than high sea ice Atmospheric model hierarchies that progressively add individual processes have a long history in providing dynamical insight for modeling the atmosphere. Similarly, coupled model hierarchies that progressively add individual ocean processes can provide insights into the workings of the coupled climate system, however such hierarchies have not been available except for a non-dynamical slab ocean model. Because of the missing processes, surface flux corrections must be added to produce a target climate. In this project, we managed to overcome this problem and develop a globally coupled ocean model hierarchy in CESM that can turn on and off the processes of mixed-layer entrainment and Ekman flow. We used the hierarchy to study the impact of Arctic sea-ice loss on the climate system. We find that the effect of mixed-layer entrainment on ocean heat uptake influences the atmospheric circulation by shifting the latitudinal positions of the mid-latitude westerly jet and the Intertropical Convergence Zone (ITCZ). In quadrupled CO 2 experiments, we studied how air-sea coupling affects the response of tropical rainfall under global warming. In order to identify the importance of individual ocean processes, we used the hierarchy of ocean models to separate the effects of seasonal mixed-layer entrainment, wind-driven Ekman flows, and frictional flows. We showed that including Ekman and frictional flows allows our simulation to produce the Pacific Ocean's enhanced equatorial warming pattern and equatorward ITCZ contraction noted in previous climate simulations. We also showed that the frictional flow, which has yet to receive much attention, is as important as the Ekman flow in generating equatorial heat convergence.
Resistive wall implementation for tokamaks has been achieved by both a 'thin wall' approximation in which time-varying fields are matched across a boundary for all modes; and, by meshing the region from the first wall to the outer boundary and setting certain fields to mimic a resistive wall. Both implementations were tested on existing tokamaks.
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks. Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.
Correlated sampling using batch statistics with MCNP6’s tally fluctuation charts (TFCs) is a powerful means of reducing the statistical uncertainties of various combinations of tallies. Because MCNP6 prints a maximum of 20 entries in the TFCs, at least five inputs must be run to obtain the recommended number of at least 100 batches. A new software tool, MAKE_COSUBS, reads an MCNP6 input file and sets up a sequence of input files to be run. The user runs MAKE_COSUBS for the unperturbed and all perturbed MCNP6 inputs, runs MCNP6 for the inputs, and finally analyzes the outputs using COSUBS.
In this poster the Rubin Observatory's Community Science team (CST) presents its current suite of tutorials, which are designed to help people make use of simulated data sets in preparation for the upcoming Legacy Survey of Space and Time (LSST). We will show examples of the tutorial contents, provide custom learning modules for different astronomical fields, and describe the online environment for data analysis (the Rubin Science Platform; RSP). We will also supply a checklist for how to obtain an RSP account and access the tutorials. All are welcome to drop by the poster or the Rubin booth in the exhibit hall with questions.
Malicious actors frequently use proxies and VPNs to evade detection and hide their origin. Current challenges to information security include the use of residential proxies to blend in with normal traffic and Man-in-the-Middle phishing proxies that are used to compromise accounts protected with mult-factor authentication. We advance a taxonomy and feature set for the identification of proxied traffic based on the network layer where proxying occurs. We describe how these features apply to common proxy types and how to use these features in the classification of the proxied traffic. Collection of these additional features is feasible using existing network sensors and web servers, while only adding about 30% volume to commonly deployed network sensor logs.
A multidisciplinary team at Argonne National Laboratory explores the application of advanced technologies to enhance knowledge transfer and retention within the nuclear safeguards domain. Specifically, it examines the feasibility of leveraging secure large language models (LLMs) to streamline the creation of e-learning modules for the U.S. National Nuclear Security Administration (NNSA) Office of International Nuclear Safeguards (NA-241). The initiative addresses the critical need for preserving institutional memory and accelerating skill development amidst the imminent retirement of senior professionals in the field in addition to supporting good knowledge management practices. The project integrates instructional design theory with cutting-edge AI technologies to transform curated document sets from the Safeguards Knowledge Repository (SKR) into modular online courses. By automating the generation of learning objectives and instructional content, the effort aims to reduce manual effort while maintaining high-quality educational outcomes. A limited measure of human supervision, however, ensures accuracy, relevance, and alignment with NNSA’s strategic priorities. Key findings highlight the potential of AI-assisted course generation to support safeguards professionals by creating structured, interactive learning experiences. The report underscores the importance of SME validation to address limitations in AI-generated content, such as terminology errors and gaps in coverage. Recommendations include adopting a structured workflow combining LLM acceleration with expert oversight to ensure accuracy, usability, and alignment with learner needs. This work demonstrates Argonne’s commitment to advancing national security and scientific excellence through innovative knowledge management solutions.
The primary research goal of this 2-year project is to demonstrate the feasibility of an innovative approach to measure reactive plasma species in the most challenging settings: right on the surface of water and under water. We propose to use the Brewster angle-cavity ringdown spextroscopy (BA-CRDS). Research objectives include: 1) Measurements of OH and NO in the plasma gas-phase zone (Z1) using different types of LTP sources including continuous and pulsed plasma jets, sheets, torches, etc. 2) Measurements of OH and NO in the multiphase reaction zones, right on the surface of water (Z2) and under water (Z3). 3) RS formation and loss mechanisms and reaction dynamics in the three zones. 4) Generalization of the proposed system to be configurable with other selected innovative techniques for other LTP parameters; invitation of selected PIs to form a strong collaborative team to pursue the follow-up effort (Category-2).
This report provides a compendium of references for Target Set and Vital Area Identification as well as an Excel sheet to aid in design vendor choice of method and understanding of required design documentation.
NOvA is a long-baseline neutrino experiment studying neutrino oscillations by detecting neutrinos from the NuMI beam at Fermilab. Its physics analysis relies on accurate prong segmentation, which involves matching each hit to its source particle and identifying the particle type. This task has commonly been addressed using a combination of traditional clustering algorithms and convolutional neural networks (CNNs). However, NOvA’s detector design presents data as two sparse and decoupled 2D images (XZ and YZ views) rather than a native 3D representation, posing a significant challenge for traditional CNN-based models. In this talk, we propose a novel neural network based on the Point Set Transformer. By treating detector hits as sparse point clouds and implementing a cross-view attention mechanism, our model enables efficient information mixing between both views. Evaluated on NOvA simulated data, our model achieves superior accuracy while requiring significantly fewer computational resources compared to other models. Furthermore, the model demonstrates great performance when applied to Liquid Argon Time Projection Chamber (LArTPC) data, which shows its potential as a universal prong segmentation algorithm for multiple view neutrino detectors.