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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Cloud processing of dimethyl sulfide (DMS) oxidation products limits sulfur dioxide (SO 2 ) and carbonyl sulfide (OCS) production in the eastern North Atlantic marine boundary layer

Dimethyl sulfide (DMS) is the major sulfur species emitted from the ocean. The gas-phase oxidation of DMS by hydroxyl radicals proceeds through the stable, soluble intermediate hydroperoxymethyl thioformate (HPMTF), eventually forming carbonyl sulfide (OCS) and sulfur dioxide (SO 2 ). Recent work has shown that HPMTF is efficiently lost to marine boundary layer (MBL) clouds, thus arresting OCS and SO 2 production and their contributions to new-particle formation and growth events. To date, no long-term field studies exist to assess the extent to which frequent cloud processing impacts the fate of HPMTF. Here, we present 6 weeks of measurements of the cloud fraction and the marine sulfur species methanethiol, DMS, and HPMTF made at the Atmospheric Radiation Measurement (ARM) research facility on Graciosa Island, Azores, Portugal. Using an observationally constrained chemical box model, we determine that cloud loss is the dominant sink of HPMTF in this region of the MBL during the study, accounting for 79 %–91 % of HPMTF loss on average. When accounting for HPMTF uptake to clouds, we calculate campaign average reductions in DMS-derived MBL SO 2 and OCS of 52 %–60 % and 80 %–92 % for the study period. Using yearly measurements of the site- and satellite-measured 3D cloud fraction and DMS climatology, we infer that HPMTF cloud loss is the dominant sink of HPMTF in the eastern North Atlantic during all seasons and occurs on timescales faster than what is prescribed in global chemical transport models. Accurately resolving this rapid loss of HPMTF to clouds has important implications for constraining drivers of MBL new-particle formation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Laboratory and field assessment of mid-infrared absorption (MIRA) instrument performance for methane and ethane dry mole fractions

Concurrent measurements of methane (CH 4 ) and ethane (C 2 H 6 ) can be used to identify and separate methane sources, as ethane is present in thermogenic sources (e.g., oil and natural gas) but not in biogenic sources (e.g., agriculture). In this study, we evaluated the performance of multiple Aeris MIRA Ultra instruments (Versions 1 and 2) through controlled laboratory tests and tower-based deployments under field conditions. The systems were modified with an external pump, flow control, a Nafion dryer, and a custom-built auxiliary box to automate the system and transmit near real-time data. We determined the best calibration approach for our application, given practical limitations, to be a full calibration cycle (with ambient and high calibration cylinders) about once per day and an ambient calibration cylinder sampled hourly. Measurement uncertainty was assessed, including the uncertainty due to instrument noise as a function of calibration frequency, uncertainty in the water vapor correction, and cylinder assignment uncertainty. Instrument noise was the dominant source of uncertainty for C 2 H 6 , while the water vapor correction dominated the CH 4 uncertainty. For Version 2 systems with hourly calibrations and a Nafion dryer with counterflow, the mean total uncertainty, including both systematic errors and noise, of hourly averages was 0.8–3.0 ppb CH 4 and 0.35–0.37 ppb C 2 H 6 . Laboratory intercomparisons showed network compatibility within 1.2 ppb CH 4 and 0.23 ppb C 2 H6, and a collocated deployment with a NOAA Picarro system agreed within 1.8 ppb CH 4 . Instrument noise varied substantially amongst the instruments, with errors reaching up to 11 ppb CH 4 and 2 ppb C 2 H 6 for hourly means, with similar variability indicated in a 50 h cylinder test. With appropriate engineering and calibration, the Aeris MIRA Ultra has the potential to distinguish regional methane emission sources in many field settings.

03 NATURAL GAS↗
DOE OSTI2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ADEPT: A Pedagogical Framework for Integrating Agentic AI with Deterministic Scientific Workflows

The integration of Large Language Models (LLMs) into scientific research promises to accelerate discovery, yet a significant gap remains between the dynamic reasoning of Artificial Intelligence (AI) agents and the static, deterministic nature of canonical scientific workflows. This paper introduces ADEPT (Agentic Discovery and Exploration Platform for Tools), a reference architecture and pedagogical framework explicitly designed to bridge this gap. ADEPT's primary mission is to provide a transparent, "glass-box" environment where researchers and engineers can learn to effectively wrap established scientific software (e.g., BLAST, Nextflow pipelines) and compose it into reliable, agent-driven workflows. We describe its modular, multi-server architecture, which leverages the Model Context Protocol (MCP) for tool serving, LangGraph for robust agentic orchestration, and a secure nsjail-based sandbox for safe code execution. By prioritizing architectural clarity, safety, and modularity, ADEPT serves as an extensible blueprint for building trustworthy AI-augmented systems and fosters the collaborative development necessary to responsibly employ agentic AI for science. We provide practical examples of how to adapt and extend this framework, highlighting its utility in workforce development and AI-readiness capabilities across research and development projects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Edge ML for CAN bus intrusion detection in AVs

Autonomous Vehicles (AVs) are revolutionizing transportation, but their reliance on interconnected cyber-physical systems exposes them to unprecedented cybersecurity risks. This study addresses the critical challenge of detecting real-time cyber intrusions in self-driving vehicles by leveraging a dataset from the Udacity self-driving car project. We simulate four high-impact attack vectors, Denial of Service (DoS), spoofing, replay, and fuzzy attacks, by injecting noise into spatial features (e.g., bounding box coordinates) to replicate adversarial scenarios. We develop and evaluate two lightweight neural network architectures (NN-1 and NN-2) alongside a logistic regression baseline (LG-1) for intrusion detection. The models achieve exceptional performance, with NN-2 attaining an AUC score of 93.15% and 93.15% accuracy, demonstrating their suitability for edge deployment in AV environments. Through explainable AI techniques, we uncover unique forensic fingerprints of each attack type, such as spatial corruption in fuzzy attacks and temporal anomalies in replay attacks, offering actionable insights for feature engineering and proactive defense. Visual analytics, including confusion matrices, ROC curves, and feature importance plots, validate the models' robustness and interpretability. This research sets a new benchmark for AV cybersecurity, delivering a scalable, field-ready toolkit for Original Equipment Manufacturers (OEMs) and policymakers. By aligning intrusion fingerprints with SAE J3061 automotive security standards, we provide a pathway for integrating machine learning into safety-critical AV systems. Our findings underscore the urgent need for security-by-design AI, ensuring that AVs not only drive autonomously but also defend autonomously. This work bridges the gap between theoretical cybersecurity and life-preserving engineering, offering a leap toward safer, more secure autonomous transportation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Warm boundary-layer cloud properties and cloud susceptibilities product at the ARM ENA observatory

Cloud liquid water path, albedo, and cloud fraction susceptibilities for marine warm boundary-layer clouds are quantified using the Meteosat retrieved cloud microphysical properties at the ARM Eastern North Atlantic (ENA) observatory in the Azores. The Meteosat cloud retrievals are based on the methods applied by the Clouds and the Earth’s Radiant Energy System (CERES) project, which are specifically tailored to support the ARM user facility over the ARM ground-based observation sites (enavisstpx2dm11minnisX1.c1). Cloud susceptibility is quantified as the slope between cloud properties and Nd within a 1-degree-by-1-degree grid box at each satellite time step using a least-square regression. To reduce uncertainties associated with the adiabatic assumption in the Nd retrieval, for each cloud object, we exclude cloudy pixels at the cloud edge, defined as those adjacent to cloud-free pixels. To minimize influence of cloud heterogeneity and co-variability on the retrieved cloud susceptibility, cloud properties are averaged to 0.25-degree resolution using the 3-km pixel-level retrieval, and the 1-degree cloud susceptibilities are estimated using the smoothed data. Cloud type classification is applied to distinct cloud object, labeling all contiguous cloudy pixels. Marine warm boundary-layer clouds are defined as clouds with 90% of cloud tops below 3km, and Meteosat retrieved cloud phase as liquid.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Eucalyptus – An Analysis Suite for Fault Trees with Uncertainty Quantification

Eucalyptus is a novel code developed at Lawrence Livermore National Laboratory to incorporate uncertainty quantification into Fault Tree Analysis (FTA). This tool addresses the challenge of imperfect knowledge in “grey-box” systems by allowing analysts to incorporate and propagate uncertainty from component-level assessments to system-level effects. Eucalyptus facilitates a consistent evaluation of the impact of subject matter expert judgment and knowledge gaps on overall system response by Monte Carlo generation of possible system fault trees, sampling probabilities of the existence of subsystems and components. Here, the code supports the specification of fault trees through text and allows export to various formats, including auto-generated images, easing analysis and reducing errors. It has undergone extensive verification testing, demonstrating its reliability and readiness for deployment, and leverages on-node parallelism for rapid analysis. Example analyses are shown that include the identification of system failure paths and quantification of the value of further information about system components.

Fault Tree Analysis↗
DOE OSTI2026

Evaluating Protection System Performance for a Real-World Weak Grid Area With High Inverter-Based Resources

This paper evaluates an existing protection scheme implemented in a real-world weak grid area with a high penetration of inverter-based resources (IBRs). The study aims to assess the reliability and adequacy of protection schemes originally designed for traditional synchronous machine systems and determine whether they can continue to operate reliably in systems with high levels of IBRs. Hardware relays are tested using a controller-hardware-in-the-loop setup. PSCAD electromagnetic transient simulation with an IBR original equipment manufacturer black-box model is used to perform fault studies and generate COMTRADE data, which are replayed by a realtime digital simulator (RTDS) to feed input to the hardware relays. Three scenarios are analyzed: normal operation, an N-1 contingency, and an IBR-only scenario. The evaluation results reveal the following: 1) the protection scheme remains reliable under normal conditions and N-1 contingencies and 2) in IBRonly scenarios, differential protection (87L) continues to operate reliably, whereas local protection elements, such as distance and directional elements, fail because of the lack of regulated negative sequence current contributed by IBRs. These findings provide utilities with valuable insights for improving their protection systems in high-IBRs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Automated Classification of Vehicle Movements at Signalized Intersections Using Vehicle Trajectories

Accurate vehicle movement classification through signalized intersections is of paramount importance to the analysis of intersection performance and the optimization of traffic control strategies. Conventional techniques for tracking vehicle turning movements depend on infrastructure-based strategies like human counts, loop detectors, and video analytics, all of which are costly, prone to errors, and spatially constrained. High-frequency trajectory data can be utilized to determine vehicle movement patterns in a scalable and infrastructure-independent method due to the adoption of connected vehicles (CVs). In recent years, several studies have utilized connected vehicle data to generate performance measures. Most of the trajectory-based performance measures approaches, however, require map matching-i.e., extracting geospatial references from maps to identify the movements that individual vehicles make at a signalized intersection. These approaches are often time-consuming and hinder scalability since geographic features need to be provided for an analysis to be conducted. Map matching methods are prone to errors as different map versions change these geographic features. This research presents a novel automatic classification pipeline that uses CV trajectory data to classify vehicle movements at signalized crossings, specifically pass-through left-turn and right-turn maneuvers. The process starts by filtering trips that cross a spatial bounding box that has been defined at the target intersection. Approach and departure headings for each trajectory crossing the boundary are computed and are clustered together to identify dominant movements. The proposed algorithm is used to classify the movement of vehicles at 10 intersections in the state of California, and the results indicate that the algorithm can classify movements at these intersections with varying traffic volumes and road network configurations, all in a map-less framework with no need for conflation of vehicle trajectories to a digital base map.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Solar Heat for Industrial Processes: Integration with Chemical Reactors

The integration of solar thermal systems with chemical reactors has been proposed as part of a larger effort to develop and deploy solar heat for industrial processes (SHIP) technologies. A strong motivation for SHIP processes and technologies is the potential for high thermal efficiency coupled with low-cost thermal energy storage (TES) which can enable commercial deployment of such systems. While there are different ways to categorize SHIP technologies, one important such distinction is between directly irradiated systems and indirect off-sun process driven by a SHIP system. While directly irradiated systems can provide high thermal efficiencies and high fluxes, they usually require complex engineering solutions due to the need for redesigning the established processes and unit operations. In most cases, it is also more challenging to couple such a process to a TES system, losing some of the benefits of SHIP. On the other hand, using a SHIP system to drive an industrial process off-sun can allow better integration with existing process chains, easier TES capabilities, and potential for more applications fitting a specific SHIP technology. However, the integration of SHIP systems with the industrial processes is not fully explored in detail, especially in the case of high-temperature processes such as reforming, cracking, cement manufacturing, and iron/steelmaking. Many of these systems require heating fluxes of >50 kW/m^2, supplied via combustion of hydrocarbons in a fire box and benefitting from radiative heat transfer between the flue gases and the reaction zones. As such, using SHIP systems for such processes is more complex than providing the same thermal input in the form of a heat transfer medium (HTM) entering the reactor, kiln, or furnace. Moreover, in case convective heat transfer using SHIP is envisioned, for example using supercritical CO2 as the HTM from a particle receiver, the thermal integration might be more challenging than initially envisioned: lower heat transfer coefficients and limited approach temperature might require large flow rates, causing a mismatch between the process thermal requirements and the thermal capacity of the SHIP system. In addition, even if the heat exchange between SHIP and reactor is effective, there is still a cold leg HTM at the reaction temperature or slightly below it. Chemical plants usually include a set of heat exchangers, heat recovery steam generators, and even power generation units - in a tightly integrated design - to recover the flue gases which are eventually vented. With SHIP systems mostly operating on a closed HTM loop, bottoming the cold leg is crucial. In this talk, we will present different modeling results for a variety of syngas production reactions, using catalytic and chemical looping processes, and discuss some of the challenges and design considerations for off-sun chemical reactors using SHIP systems.

14 SOLAR ENERGY↗
DOE OSTI2024

Neural Networks for Prediction of Complex Chemistry in Water Treatment Process Optimization

Water chemistry plays a critical role in the design and operation of water treatment processes. Detailed chemistry modeling tools use a combination of advanced thermodynamic models and extensive databases to predict phase equilibria and reaction phenomena. The complexity and formulation of these models preclude their direct integration in equation-oriented modeling platforms, making it difficult to use their capabilities for rigorous water treatment process optimization. Neural networks (NN) can provide a pathway for integrating the predictive capability of chemistry software into equation-oriented models and enable optimization of complex water treatment processes across a broad range of conditions and process designs. Herein, we assess how NN architecture and training data impact their accuracy and use in equation-oriented water treatment models. We generate training data using PhreeqC software and determine how data generation and sample size impact the accuracy of trained NNs. The effect of NN architecture on optimization is evaluated by optimizing hypothetical black-box desalination processes using a range of feed compositions from USGS brackish water data set, tracking the number of successful optimizations, and testing the impact of initial guess on the final solution. Our results clearly demonstrate that data generation and architecture impact NN accuracy and viability for use in equation-oriented optimization problems.

Dudchenko, Alexander V↗
DOE OSTI2024

PV Inverter Availability from the U.S. PV Fleet

In the PV Fleet Performance Data Initiative, we partner with photovoltaic (PV) fleet owners to collect time-series PV production data, and publish aggregated, anonymized results. An assessment of system availability is conducted on 1128 systems which passed our data quality checks, and include cumulative energy meter data. Overall inverter availability is low in the first 6 months of system performance before reaching steady-state by the end of the first year. System-level aggregated data shows a median (P50) system availability of 0.99, and a lower P90 value of 0.95. A dependence on system size is also identified, with better inverter availability results for smaller PV systems. Potential causes of this effect may include the selection of inverter itself: smaller inverters 6kW-250kW showed better average availability than inverters 300kW-5MW. The elimination of string combiner boxes and lower energy impact when one particular inverter goes off-line are potential benefits of a string inverter-based PV system architecture. DNV also analyzed availability data from over 1100 operating systems and found similar trends. DNV's P50 industry guidance on expected availability has been updated to reflect the data and the following observations: utility scale systems have lower availability than DG systems, availability is lower in first year compared to subsequent years, and that actual availability is lower than expected.

fleet↗
DOE OSTI2024

An Accelerated Clip Algorithm for Unstructured Meshes: A Batch-Driven Approach

The clip technique is a popular method for visualizing complex structures and phenomena within 3D unstructured meshes. Meshes can be clipped by specifying a scalar isovalue to produce an output unstructured mesh with its external surface as the isovalue. Similar to isocontouring, the clipping process relies on scalar data associated with the mesh points, including scalar data generated by implicit functions such as planes, boxes, and spheres, which facilitates the visualization of results interior to the grid. In this paper, we introduce a novel batch-driven parallel algorithm based on a sequential clip algorithm designed for high-quality results in partial volume extraction. Our algorithm comprises five passes, each progressively processing data to generate the resulting clipped unstructured mesh. The novelty lies in the use of fixed-size batches of points and cells, which enable rapid workload trimming and parallel processing, leading to a significantly improved memory footprint and run-time performance compared to the original version. On a 32-core CPU, the proposed batch-driven parallel algorithm demonstrates a run-time speed-up of up to 32.6x and a memory footprint reduction of up to 4.37x compared to the existing sequential algorithm. The software is currently available under an open-source license in the VTK visualization system.

Tsalikis, Spiros↗
DOE OSTI2024

Construction of a Cosmic Ray Telescope for the hpDIRC Radiators at the Electron Ion Collider

Nuclear physics pertains to the understanding of the structure and dynamics of the nuclei of atoms, accounting for almost all of the visible matter in the universe. The future Electron-Ion Collider (EIC) will play a crucial role in discovering new laws of Nuclear physics. A pivotal aspect of the EIC is exceptional particle identification (PID). Identifying charged hadrons in their final state can be done with special types of Cherenkov Detectors. One type of detector utilizes the Detection of Internally Reflected Cherenkov radiation (DIRC) phenomena. Able to provide precise PID separation up to relatively high momenta, DIRC radiators, made of synthetic fused silica, produce Cherenkov radiation when hit by high energy electromagnetically charged particles. In this thesis we present details of the construction of a cosmic ray telescope (CRT) to utilize high energy muons from cosmic rays to test DIRC radiators. These types of radiators will be used for the high-performance DIRC (hpDIRC) to be used in the ePIC detector in the future EIC. We go into detail on the DIRC process and the individual pieces of hardware required to make the CRT. Lots of labor was used for certain parts of the CRT, specifically the construction of a dark box for the DIRC radiators. The CRT construction and implementation of all of its components are planned to be completed before 2025.

Shankman, Nathan↗
DOE OSTI2024

Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching

Diffusion MRI (dMRI) non-invasively maps brain white matter yet necessitates denoising due to low signal-to-noise ratios. Patch2Self (P2S) employing self-supervised techniques and regression on a Casorati matrix effectively denoises dMRI images and has become the new de-facto standard in this field. P2S however is resource intensive both in terms of running time and memory usage as it uses all voxels (n) from all-but-one held-in volumes (d-1) to learn a linear mapping Phi : \mathbb R ^ n x(d-1) \mapsto \mathbb R ^ n for denoising the held-out volume. The increasing size and dimensionality of higher resolution dMRI acquisitions can make P2S infeasible for large-scale analyses. This work exploits the redundancy imposed by P2S to alleviate its performance issues and inspect regions that influence the noise disproportionately. Specifically this study makes a three-fold contribution: (1) We present Patch2Self2 (P2S2) a method that uses matrix sketching to perform self-supervised denoising. By solving a sub-problem on a smaller sub-space so called coreset we show how P2S2 can yield a significant speedup in training time while using less memory. (2) We present a theoretical analysis of P2S2 focusing on determining the optimal sketch size through rank estimation a key step in achieving a balance between denoising accuracy and computational efficiency. (3) We show how the so-called statistical leverage scores can be used to interpret the denoising of dMRI data a process that was traditionally treated as a black-box. Experimental results on both simulated and real data affirm that P2S2 maintains denoising quality while significantly enhancing speed and memory efficiency achieved by training on a reduced data subset.

Fadnavis, Shreyas↗
DOE OSTI2024

Emergency Use Respirators in TRA-670

The project was based on the research of the history of the emergency use air purifying respirators (APRs) in TRA-670. Interviews, inspections, and document reviews were conducted. The interviews, inspections, and documents revealed that there were no current documents as to why the emergency use APRs were placed into TRA-670. The APRs were placed into green emergency boxes and stashed around the plant. Though they are checked to ensure they are not messed with, they have not been cleaned or re-verified since 2016 (some since 2014). When interviewees were asked, in an emergency, when they would reach for an APR over an emergency use SCBA, the consensus was that they would not. They would evacuate the facility as quickly as possible. If emergency respiratory protection were needed, a SCBA would be used. This is because, in an emergency scenario, one would not know what is in the air, so the utilization of a SCBA would be the safest option. Overall, the recommendation would be for the removal of the emergency use APRs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Intern Poster: AIBOMs in Automated Defense: Capabilities & Challenges

AI is increasingly used in critical infrastructure, but it's often a “black box” that is difficult to understand. AIBOMs, or AI Bill of Materials, are one way to increase AI transparency and accountability. They can also be used in automated cyber defense. AIBOMs are a new concept with challenges to widespread adoption, but their potential in cybersecurity makes them a worthwhile investment.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

PIP-II Linac Cryogenic Distribution System Design Challenges

The PIP-II linac Cryogenic Distribution System (CDS) is characterized by extremely small heat inflows and robust mechanical design. It consists of a Distribution Valve Box (DVB), Intermediate Transfer Line, Tunnel Transfer Line, comprising 25 Bayonet Cans, and ends with a Turnaround Can. Multiple helium streams, each characterized by distinct helium parameters, flow through each of these elements. The CDS geometry allows maintaining an acceptable pressure drop for each helium stream, considering the planned flows and helium parameters in different operation modes. This is particularly crucial for the return line of helium vapors, which return from cryomodules to the cold compressors and thus have very restrictive pressure drop requirements. On both sides of the DVB there are fixed supports for process pipes. One of the DVB design challenges was to route the process pipes in such a way that their shape provided sufficient compensation for thermal shrinkage. This ensures th at the forces resulting from thermal shrinkage acting on the cryogenic valves remain at a level acceptable to the manufacturer. The required thermal budget of the CDS was achieved by thermo-mechanical optimization of its components, like process pipes fixed supports in Bayonet Cans.

43 PARTICLE ACCELERATORS↗