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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Quantifying Solar PV Variability at Multiple Timescales for Power Systems Studies: Preprint

Increasing penetration of solar photovoltaics (PV) has concerned power system operators about their ability to balance load and generation over multiple timescales and maintain acceptable frequency profile because of the variability and uncertainty associated with solar PV. This paper focuses on developing metrics to quantify solar PV variability at multiple temporal scales ranging from seconds to an hour to help power system operators and planners better understand and account for solar PV variability under high solar PV penetration. Fourier coefficients of solar irradiation spectrum and average size of solar irradiation ramps over a day are the two metrics that are used to quantity the variability. It is shown that by using the two metrics the days with highest and lowest solar irradiation variability can be identified, which is useful information for operations planning or long-term power system planning studies. The key findings of the paper are (i) single sensor-based variability quantification can significantly exaggerate the variability of a solar PV plant, particularly at seconds to 1-min temporal resolution, (ii) at 1-minute or lower temporal resolutions, single sensor-based variability quantification of an entire plant can be fairly accurate, with the accuracy increasing as the temporal resolution reduces from 1-minute to 1-hour, (iii) at each 1-minute and lower temporal resolutions, maximum solar irradiation ramps appear to be independent of the geographical location of the sensor, and (iv) significant reduction in solar PV plant variability over a region with multiple plants is possible at all temporal scales, but more high quality, high temporal resolution data is needed to quantify the reduction with high confidence.

photovoltaics↗
DOE OSTI2021

Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach

The broader ambition of this article is to popularize an approach for the fair distribution of the quantity of a system's output to its subsystems while allowing for underlying complex subsystem level interactions. Particularly, we present the use of this framework on a very specific (but generalizable) application, interesting for a more general AI audience. Here, we detail a data-driven approach to vehicle price modeling and its component price estimation by leveraging a combination of concepts from machine learning and game theory. We show an alternative to common teardown methodologies and surveying approaches for component and vehicle price estimation at the manufacturer's suggested retail price (MSRP) level that has the advantage of bypassing uncertainties involved in gathering teardown data, the need to perform expensive and biased surveying, and the need to perform retail price equivalent or indirect cost multiplier adjustments to mark up direct manufacturing costs to MSRP. This novel exercise not only provides accurate pricing of the technologies at the customer level, but also shows the, a priori known, large gaps in pricing strategies between manufacturers, vehicle classes, market segments, etc. There is also clear interaction between the price of technologies and other specifications present in vehicles. Those results are indication that old methods of manufacturer-level component costing, aggregation, and application of flat and rigid adjustment factors should be carefully examined. The findings are based on a database developed by Argonne, which includes over 64,000 vehicles covering MY1990 to MY2020 with hundreds of vehicle specs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Decentralized Schemes with Overlap for Solving Graph-Structured Optimization Problems

We present a new algorithmic paradigm for the decentralized solution of graph-structured optimization problems that arise in the estimation and control of network systems. A key and novel design concept of the proposed approach is that it uses overlapping subdomains to promote and accelerate convergence. We show that the algorithm converges if the size of the overlap is sufficiently large and that the convergence rate improves exponentially with the size of the overlap. The proposed approach provides a bridge between fully decentralized and centralized architectures and is flexible in that it enables the implementation of asynchronous schemes, handling of constraints, and balancing of computing, communication, and data privacy needs. The proposed scheme is tested in an estimation problem for a 9241-node power network and we show that it outperforms the alternating direction method of multipliers.

asynchronous↗
DOE OSTI2021

Decentralized Low-Rank State Estimation for Power Distribution Systems

This article considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix completion methodology. Matrix completion has been shown to be an effective technique in state estimation that exploits the low dimensionality of the power system measurements to recover missing information. This technique can utilize an approximate (linear) load flow model, or it can be used with no physical models in a network where no information about the topology or line admittance is available. The direct application of matrix completion algorithms requires solving a semi-definite programming (SDP) problem, which becomes computationally challenging for large networks. We therefore develop a decentralized algorithm that capitalizes on the popular proximal alternating direction method of multipliers (proximal ADMM). The method allows us to distribute the computation among different areas of the network, leading to a scalable algorithm. By doing all computations at individual control areas and only communicating with neighboring areas, the algorithm eliminates the need for data to be sent to a central processing unit and thus increases efficiency and contributes to the goal of autonomous control of distribution networks. We illustrate the advantages of the proposed algorithm numerically using standard IEEE test cases.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2023

The need for an integrated multi-OMICs approach in microbiome science in the food system

Microbiome science as an interdisciplinary research field has evolved rapidly over the past two decades, becoming a popular topic not only in the scientific community and among the general public, but also in the food industry due to the growing demand for microbiome-based technologies that provide added-value solutions. Microbiome research has expanded in the context of food systems, strongly driven by methodological advances in different -omics fields that leverage our understanding of microbial diversity and function. However, managing and integrating different complex -omics layers are still challenging. Within the Coordinated Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), a project supported by the European Commission, the workshop “Metagenomics, Metaproteomics and Metabolomics: the need for data integration in microbiome research” gathered 70 participants from different microbiome research fields relevant to food systems, to discuss challenges in microbiome research and to promote a switch from microbiome-based descriptive studies to functional studies, elucidating the biology and interactive roles of microbiomes in food systems. A combination of technologies is proposed. This will reduce the biases resulting from each individual technology and result in a more comprehensive view of the biological system as a whole. Although combinations of different datasets are still rare, advanced bioinformatics tools and artificial intelligence approaches can contribute to understanding, prediction, and management of the microbiome, thereby providing the basis for the improvement of food quality and safety.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

PAGANI: a parallel adaptive GPU algorithm for numerical integration

We present a new adaptive parallel algorithm for the challenging problem of multi-dimensional numerical integration on massively parallel architectures. Adaptive algorithms have demonstrated the best performance, but efficient many-core utilization is difficult to achieve because the adaptive work-load can vary greatly across the integration space and is impossible to predict a priori. Existing parallel algorithms utilize sequential computations on independent processors, which results in bottlenecks due to the need for data redistribution and processor synchronization. Our algorithm employs a high-throughput approach in which all existing sub-regions are processed and sub-divided in parallel. Repeated sub-region classification and filtering improves upon a brute-force approach and allows the algorithm to make efficient use of computation and memory resources. A CUDA implementation shows orders of magnitude speedup over the fastest open-source CPU method and extends the achievable accuracy for difficult integrands. Our algorithm typically outperforms other existing deterministic parallel methods.

Sakiotis, Ioannis↗
DOE OSTI2022

A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning

Computational models that formalize complex human behaviors enable study and understanding of such behaviors. However, collecting behavior data required to estimate the parameters of such models is often tedious and resource intensive. Thus, estimating dataset size as part of data collection planning (also known as Sample Size Determination) is important to reduce the time and effort of behavior data collection while maintaining an accurate estimate of model parameters. In this paper, we present a sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL) model of human behavior, in two cases: 1) pre-hoc experiment design—conducted in the planning stage before any data is collected, to guide the estimation of how many samples to collect; and 2) post-hoc dataset analysis—performed after data is collected, to decide if the existing dataset has sufficient samples and whether more data is needed. Here, we validate our approach in experiments with a realistic model of behaviors of people with Multiple Sclerosis (MS) and illustrate how to pick a reasonable sample size target. Our work enables model designers to perform a deeper, principled investigation of effects of dataset size on IRL.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Janus v1.0

Janus provides a software framework for lightweight container management and orchestration. It's primary use cases are around deploying containerized services for high-performance data movement needs. Thus, Janus differentiates itself from systems like Kubernetes by tailoring the deployment of containers around network, storage, and host tuning optimizations. Janus uses the concept of profiles to capture repeatable deployment patterns and applies them to container execution across one or more endpoints. A Janus Agent component provides remote host tuning and monitoring capabilities.

Essiari, Abdelilah [Lawrence Berkeley National Lab↗
DOE OSTI2023

Quantifying Uncertainty in Ice Particle Velocity–Dimension Relationships Using MC3E Observations

Abstract Ice particle terminal fall velocity ( V t ) is fundamental for determining microphysical processes, yet remains extremely challenging to measure. Current theoretical best estimates of V t are functions of Reynolds number. The Reynolds number is related to the Best number, which is a function of ice particle mass, area ratio ( A r ), and maximum dimension ( D max ). These estimates are not conducive for use in most models since model parameterizations often take the form , where ( α , β ) depend on habit and D max . A previously developed framework is used to determine surfaces of equally plausible ( α , β ) coefficients whereby ice particle size/shape distributions are combined with V t best estimates to determine mass- ( V M ) or reflectivity-weighted ( V Z ) velocities that closely match parameterized V M ,SD or V Z ,SD calculated using the ( α , β ) coefficients using two approaches. The first uses surfaces of equally plausible ( a , b ) coefficients describing mass ( M )–dimension relationships (i.e., ) to calculate mass- or reflectivity-weighted velocity from size/shape distributions that are then used to determine ( α , β ) coefficients. The second investigates how uncertainties in A r , D max , and size distribution N ( D ) affect V M or V Z . For seven of nine flight legs flown on 20 and 23 May 2011 during the Mesoscale Continental Convective Clouds Experiment (MC3E), uncertainty from natural parameter variability—namely, the variability in ice particle parameters in similar meteorological conditions—exceeds uncertainties arising from different A r assumptions or D max estimates. The combined uncertainty between A r , D max , and N ( D ) produced smaller variability in ( α , β ) compared to varying M ( D ), demonstrating M ( D ) must be accurately quantified for model fall velocities. Primary sources of uncertainty vary considerably depending on environmental conditions. Significance Statement Ice particle fall velocity is fundamental for numerous processes within clouds, and hence is a critical property that must be accurately represented in weather and climate models. Using aircraft observations of ice particle shapes and sizes obtained in clouds behind midlatitude thunderstorms, this work develops a new framework for estimating ice particle fall velocities and their uncertainty, including quantifying the importance of different uncertainty sources from cloud microphysics measurements. Natural parameter variability contributes the most uncertainty in ice particle fall velocity estimates, although other sources can also be important contributors to uncertainty in certain conditions. Additional work examining ice particle data is needed to further understand how dependent uncertainty in certain ice particle properties are to local environmental conditions.

Meteorology & Atmospheric Sciences↗
DOE OSTI2026

Transplatformer: translating toxicogenomic profiles between generations of platforms

Background Transcriptomic profiling technologies have advanced the analysis of biological and toxicological responses. However, substantial differences in probe design, dynamic range, gene coverage, and preprocessing pipelines across platforms introduce artifacts that limit cross-study integration and hinder the reuse of historical datasets. We aim to develop computational methods for accurate cross-platform translation to maximize the value of legacy resources. Results We present TransPlatformer a deep learning framework for translating gene expression profiles across heterogeneous toxicogenomics platforms. TransPlatformer employs a novel attention-based architecture to map high-dimensional fold-change vectors from legacy microarray technologies to current platforms. Models are trained and evaluated using DrugMatrix, spanning three technological generations. We investigate mixed-tissue, single-tissue, and cross-tissue training paradigms and benchmark performance against multilayer perceptron and matrix-completion baselines. In mixed-tissue training, TransPlatformer achieves a greater than 50% reduction in mean absolute error (0.043 vs. 0.09) and nearly doubles Pearson correlation ( ≈ 0.71 vs. 0.37) relative to baseline methods. Importantly, TransPlatformer preserves rare but biologically meaningful over- and under-expressed signals, with mean absolute error below 0.22. Single-tissue models yield further improvements for well-represented organs, such as a 10% reduction in liver mean absolute error, while underscoring the need for data augmentation strategies in low-sample tissues.ra Conclusions TransPlatformer provides an effective and scalable computational solution for cross-platform transcriptomic translation. By enabling biologically faithful harmonization of gene expression data, the proposed approach facilitates the reuse of legacy toxicogenomics datasets, enhances downstream biomarker discovery, and supports more reproducible predictive modeling in toxicology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

LQCD NN S-wave @ SU(3) on C103

This data contains lattice QCD data files for one- and two-nucleon correlation functions on the C103 ensemble. It also contains lattice QCD data sets needed to compute the HAL QCD potential on the same configurations. 'LQCD NN S-wave @ SU(3) on C103' Copyright (c) 2025, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. This work is openly licensed via CC BY 4.0.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Update on the Benchmark Analysis of the Jupiter Experiments: Plutonium Moderated by Lead

The Jupiter experiment was a series of measurements in the summer of 2017 to analyze the effects of voids in lead moderated plutonium assemblies. This experiment was part of a larger campaign that took place at the National Criticality Experiments Research Center (NCERC) in collaboration with the Japan Atomic Energy Agency (JAEA) that also investigated assemblies with differing uranium enrichments. The purpose for this campaign was to provide data useful for a potential lead-bismuth cooled accelerator driven system. This data is needed due to the low number of benchmarks involving lead. Given the wide use of lead for shielding in addition to new reactor designs, there is a need for further benchmarking that this experiment could help fulfill. In addition, adding more benchmarks to the International Criticality Safety Benchmark Evaluation Project (ICSBEP) is helpful for further validation of simulation methods used for criticality safety. This summary is an update on the current progress of this analysis, and all results shown are preliminary.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Design and Modeling of an 80 bar Oxy-Combustor for Direct Fired Supercritical CO2 Applications

As supercritical CO2 power cycles for fossil energy power generation continue to generate interest, there are significant issues and unanswered questions regarding injector design, flame stabilization, wall heat transfer, CO emissions, combustion dynamics and other combustion phenomenon. For natural gas, direct-fired cycles with carbon capture it is believed that Computational Fluid Dynamics (CFD) modeling will play an essential role in the combustor design process. To accurately model turbulent reacting flows at these unique conditions, experimental data is needed to validate CFD codes and sub-models and is currently lacking at conditions relevant for these cycles. This paper presents the conceptual design and CFD simulations of an experimental 80 bar oxy-combustion facility and test article currently under construction at NETL. The facility is targeted towards the testing of a single injector, direct-fired sCO2 combustor at the 100 kW thermal output level. While these conditions do not reflect the actual Allam cycle operating conditions (300 bar) they are viewed as a stepping stone in the model validation process at supercritical conditions. Reynolds Averaged Navier-Stokes as well as Large Eddy Simulation are used to model the turbulent combustion process and aid in the design of the combustor and injector. Process parameters including oxidizer preheat temperature and combustor flowrate are investigated.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

2019 Results for Avian Monitoring of Inorganic and Organic Element Concentrations in Passerine Eggs and a Nestling Collected from Technical Area 16 Burn Grounds, Technical Area 36 Minie, and Technical Area 39 Point 6 at Los Alamos National Laboratory

In 2019, non-viable avian eggs and one nestling were opportunistically collected at Los Alamos National Laboratory (LANL) near open detonation sites located at Technical Area (TA) 16 burn grounds, TA-36 Minie, and TA-39 Point 6. These samples were evaluated for inorganic elements (mostly metals), polychlorinated biphenyls (PCBs), dioxins, and furans. A total of 26 western bluebird (Sialia mexicana) and five mountain bluebird (Sialia currucoides) egg samples and one non-viable ash-throated flycatcher (Myiarchus cinerascens) nestling were collected among the three locations of interest. Concentrations of inorganic elements observed in this study were compared with the regional statistical reference level (RSRL) which is the upper-level bounds of background concentrations (mean + three standard deviations = 99% confidence interval). Several inorganic elements were not detected in avian eggs and the majority of inorganic elements detected were below the RSRL. The few elements that exceeded the RSRL were below the lowest observable adverse effect level (LOAEL), when available. One nestling collected from TA-16 contained detectable concentrations of some dioxin and furan congeners. Octachlorodibenzodioxin-1,2,3,4,6,7,8,9 concentration exceeded the RSRL, but did not exceed the calculated tetrachlorodibenzodioxin-2,3,7,8 (TCDD) toxic equivalent LOAEL. PCBs were also detected in the nestling sample and were above the RSRL but below the LOAEL. These data suggest that inorganic and organic element concentrations in eggs and nestlings are not of ecological concern. More data are needed to make a robust assessment and to evaluate trends over time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Scorpius injector emittance measurements with solenoid scans

A classic scan solenoid can establish that the injector meets emittance requirements. Incorporating an aperture plate can validate predictions of much lower emittance. Measurements of beam parameters on the injector and ITS can inform predictions for the full LIA. Injector beam data is needed to tune the full LIA for initial turn-on and operation. Emittance measurements on the Scorpius injector are difficult because the beam is space-charge dominated. Emittance measurements on Scorpius injector and ITS will need large beam size to prevent overheating.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Unattended Single Channel Analyzer (USCA) FY20 Summary Report

In FY20 LANL was funded to design, fabricate, test and document an instrument that can be used by the IAEA for gamma ray measurements in scintillator applications. This instrument, the Unattended Single Channel Analyzer (USCA), will be part of the family of IAEA unattended instruments and completes the suite of most used data acquisition needs provided by the previously designed UNAP instrument. The USCA is designed to meet all the same IAEA requirements designed into the UMSR and UDCM. The new instrument has the same look and feel making it simple for agency staff to move between instruments. This project taps into all the design functions developed during the UMSR and UDCM development. Along with the design and fabrication of a USCA, this project included production of a prototype for IAEA testing and a complete set of documentation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Diagnosing Field Strengths and Plasma Conditions in Magnetically Insulated Transmission Lines Using Active Dopant Spectroscopy

Experimental validation data is needed to inform simulations of large pulsed power devices which are in development to understand and improve existing accelerators and inform future pulsed power capabilities. Using current spectroscopic techniques on the Z-machine, we have been unable to reliably diagnose plasma conditions and electric and magnetic fields within power flow regions. Laser ablation of a material produces a low density plasma, resulting in narrow spectroscopic line widths. By introducing a laser ablated plasma to the anode cathode gap of the Mykonos accelerator, we can monitor how the line shapes change due the current pulse by comparing these line shapes to spectral measurements taken without power flow. In this report we show several examples of measurements conducted on Mykonos on various dopant materials. We also show a negligible effect on power flow due to the presence of the ablation plasma for a range of parameters.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

2020 Results for Avian Monitoring of Inorganic and Organic Element Concentrations in Passerine Eggs and Nestlings Collected from Technical Area 16 Burn Grounds, Technical Area 36 Minie, and Technical Area 39 Point 6 at Los Alamos National Laboratory

In 2020, non-viable avian eggs and two nestling were opportunistically collected at Los Alamos National Laboratory (LANL) near open detonation sites located at Technical Area (TA) 16 burn grounds, TA-36 Minie, and TA-39 Point 6. These samples were evaluated for inorganic elements (mostly metals), dioxins, and furans. A total of six eggs and two deceased western bluebird (Sialia mexicana) nestling samples were collected among the three locations of interest. Concentrations of inorganic elements observed in this study were compared with the regional statistical reference level (RSRL) which is the upper-level bounds of background concentrations (mean + three standard deviations = 99% confidence interval). Several inorganic elements were not detected in avian eggs and all inorganic elements detected were below the RSRL and the lowest observable adverse effect level (LOAEL), when available. One nestling collected from TA-39 contained detectable concentrations of two dioxin congeners. Heptachlorodibenzodioxin-1,2,3,4,6,7,8 and octachlorodibenzodioxin-1,2,3,4,6,7,8,9 concentration exceeded the RSRL, but did not exceed the calculated tetrachlorodibenzodioxin 2,3,7,8 (TCDD) toxic equivalent LOAEL. These data suggest that inorganic and organic element concentrations in eggs and nestlings are not of ecological concern. More data are needed to make a robust assessment and to evaluate trends over time.

54 ENVIRONMENTAL SCIENCES↗