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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Role of f Electrons in the Optical and Photoelectrochemical Behavior of Ca(La 1– x Ce x ) 2 S 4 (0 ≤ x ≤ 1)

This study focuses on a solid solution series, Ca(La 1– x Ce x ) 2 S 4 (0 ≤ x ≤ 1), where the f electron density is absent in CaLa 2 S 4 and is progressively increased until it is maximized in CaCe 2 S 4 . Correspondingly, these samples, synthesized by a sealed ampule method, showed progressive variations in color ranging from gray for CaLa 2 S 4 to orange-red for CaCe 2 S 4 . The crystal structural nuances of both the end members and three solid solutions with $x$ = 0.25, 0.50, and 0.75 were established with the complementary use of synchrotron X-ray diffraction and neutron scattering. Interestingly, these data were consistent with a two-phase composition centered around each nominal solid solution stoichiometry. Optical characterization via diffuse reflectance spectroscopy and Tauc analyses showed a shrinking of the energy band gap (from the UV to vis range) when Ce was progressively introduced into the host CaLa 2 S 4 structure. Overall, these data were in concert with electronic band structure calculations, using density functional theory, which showed the progressive formation of an intermediate f band when Ce was introduced intro the structure. Photoelectrochemical measurements in an aqueous redox electrolyte, as well as surface photovoltage and Kelvin probe measurements, revealed all samples to be n-type semiconductors. The valence and conduction band edge positions of the end members and the three solid solutions could be mapped, on both the redox and vacuum reference energy scales, by combining these measurements with the optical data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Proposed Drill Sites

Proposed drill sites for intermediate depth temperature gradient holes and/or deep resource confirmation wells. Temperature gradient contours based on shallow TG program and faults interpreted from seismic reflection survey are shown, as are two faults interpreted by seismic contractor Optim but not by Oski Energy, LLC.

15 GEOTHERMAL ENERGY↗
DOE OSTI2013

BiNb{sub 3}O{sub 9}, a metastable perovskite phase with Bi/vacancy ordering: Crystal structure and dielectric properties

The perovskite, BiNb{sub 3}O{sub 9}, is a metastable phase in the Bi{sub 2}O{sub 3}:Nb{sub 2}O{sub 5} system that forms only when cooled from a liquid phase. Crystals of BiNb{sub 3}O{sub 9} prepared in this way display pseudocubic 2a×2a×2a diffraction patterns, due to non-merohedral twinning of a tetragonal a×a×2a cell, with the doubled axis oriented along all three pseudocubic axes. The structure was refined using data collected on a twinned crystal to R{sub 1}=0.034 for 318 observed reflections. BiNb{sub 3}O{sub 9} has tetragonal symmetry, P4/mmm with a=3.9459(3) Å, c=7.8919(6) Å. Partial ordering of Bi atoms and vacancies occurs, with alternate (0 0 1) planes having 28% and 42% Bi occupancies. The Bi atoms are displaced from the A-site special positions by up to 0.4 Å. The compound exhibits a relatively high permittivity value of ∼230 at room temperature, and shows a sharp increase with increasing temperature towards an apparent diffuse phase transition above ∼180 °C, with an associated frequency dependent peak in the dielectric loss. - Graphical abstract: Structure for BiNb{sub 3}O{sub 9}, showing split Bi atoms. Highlights: ► First characterisation of a new perovskite phase with potentially useful dielectric properties. ► Solving of the structure using single crystal data on a multiply twinned crystal. ► Measurement of dielectric properties that show the phase has a high dielectric permittivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Electron Density Measurements Using USPR (Final Scientific/Technical Report)

UC Davis has fabricated an ultrashort pulse reflectometer (USPR) diagnostic instrument for electron density profile measurements on compact, short duration, magnetically-confined fusion-energy concept devices such as spheromaks and FRCs. The USPR system transmits extremely short duration (~few nsec) chirped waveforms that together span 29 to 75 GHz. These chirped waveforms illuminate and reflect from the target plasma, with each frequency component reflecting from a different density layer (higher frequencies probe deeper into the plasma before reflecting). The reflected waveforms are split into roughly 42 different frequencies; time-of-flight (TOF) measurements made at each frequency with high resolution (~25 psec measurement resolution which corresponds to ~5 mm). These TOF data may then be inverted via software to generate electron density profiles with high time resolution (~10 μsec). At the heart of the system is a field programmable gate array (FPGA) based controller which collects and processes all of the USPR data in addition to generating all of the control signals required for maximum flexibility. The FPGA controller has the software flexibility to be easily reconfigured for different plasma devices, and the entire system sufficiently compact to be easily and quickly transported between devices. A high speed impulse generator was transformed into a set of three ultrashort pulse transmitter chirps using a combination of dispersive waveguide, frequency doublers and high-pass filters. A mm-wave controller was fabricated to sequentially switch between the three chirps, directing the chirps one-by-one to three different mm-wave assemblies spanning 29-75 GHz. Each mm-wave assembly consists of a high power active multiplier chain which converts the transmitter chirp to higher frequencies, and a broadband mixer which downconverts the reflected waveform to the 2-18 GHz range of the UPSR receiver. The 16-channel receiver (shared by all 3 mm-wave assemblies) was fabricated employing custom TOF modules capable of operating at a high 1 MHz sampling rate. Laboratory testing of the full system revealed the presence of unwanted harmonics from the multiplication process, with interference observed in the downconverted reflections at selected frequency channels that could not be completely filtered out. Additional interference effects arising from internal reflections within the mm-wave assemblies were minimized using a high-speed switch which served to “gate out” much of these reflections. The USPR diagnostic was transported and installed onto the HIT-SIU plasma device, becoming operational on 11/08/2022. Although designed to span 3 distinct mm-wave bands, the HIT-SIU plasmas at this time were sufficiently low density such that only the lowest of the three bands was likely to have strong plasma reflections. The system was then set to operate on only the lowest band (assembly #1), with data collected every 1 μsec rather than 3 μsec which would have been the case when cycling through all three bands. Connected to HIT-SIU, time-varying plasma reflections were observed on 9 of 16 possible frequency channels. Close examination of the data collected revealed issues previously unobserved in laboratory testing, associated with (a) reflections from the small aperture horns required for operation within the HIT-SIU device, and (b) a dependence of the recorded TOF with the threshold voltage of a given channel. Plans were made to address each of these issues before undertaking any future campaigns.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

NCSP IER 422 CED-3b Documentation

A Subcritical Copper-Reflected α-phase Plutonium (SCRαP) integral benchmark experiment has been designed and measured. In this experiment, multiplication is approximated using correlated neutron data from a detector system consisting of 3He tubes inside high density polyethylene (HDPE). Measurements were performed on various subcritical experimental configurations consisting of a weapons-grade plutonium sphere surrounded by different Cu thicknesses. In addition to the proposed base experimental configurations with Cu, additional configurations were performed with the plutonium ball nested in various thicknesses of interleaved HDPE spherical shells mixed in with the Cu shells. The HDPE is intended to provide fast neutron moderation and reflection, resulting in additional measurements with differing multiplication, spectra, and nuclear data sensitivity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

A Model on Heat Signal of Crystal Detector at Low Temperature

We present a model to calculate heat signal shapes from low-temperature bolometer attached to a crystal. This model is based on the elementary acoustic wave theory at low temperature and has been developed using modern Monte Carlo techniques. Physical processes in phonon propagation, such as transmission, scattering and reflection are considered. Using our model, the calculated time dependence of signal agrees with real experimental data. This model has applications in low-temperature rare event particle detectors for dark matter and neutrinos.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Micro Rain Radar Pro Data at the Argonne Testbed for Multiscale Observational Science obtained during the CROCUS Urban Integrated Field Laboratory

The Micro Rain Radar Pro (MRR-PRO) is a vertically pointing Ka-band Doppler radar designed to capture the fine-scale structure and evolution of precipitation. By recording the full Doppler spectrum at high temporal and spatial resolution, the MRR-PRO provides insight into both hydrometeor fall velocities and precipitation microphysics. From these spectra, key moments—reflectivity, mean Doppler velocity, spectral width, and rainfall rate—are derived and stored alongside the raw spectral data in CF/Radial 1.4-compliant files. Deployed at the Argonne Testbed for Multiscale Observational Studies (ATMOS) since November 2024, the MRR-PRO delivers vertical profiles at 70 m range resolution extending up to 4.5 km above ground level. These observations enable detailed analyses of precipitation type, intensity, and vertical structure, supporting process-level studies of cloud and precipitation dynamics in diverse weather regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Depot-Based Vehicle Data for National Analysis of Medium- and Heavy-Duty Electric Vehicle Charging

Medium- and heavy-duty vehicles (MHDVs) are a major source of greenhouse gases and local criteria air pollutants. Electrifying MHDVs may reduce these harmful emissions, which disproportionately impact disadvantaged communities. Due to their relatively high per-vehicle energy needs, consistent fleet operations, and frequent colocation of multiple vehicles at depots, MHDVs may have more spatially and temporally concentrated charging demands than light-duty passenger electric vehicles. That charging concentration means their electrification may require careful advance planning and coordination to manage potential impacts to the electrical grid via charge management or infrastructure upgrades. However, MHDV duty cycles and parking schedules are highly variable across vocations of operation, and there is a shortage of nationally representative, vocationally diverse public data describing typical MHDV operations. This report summarizes the methodology - designed with national representativeness in mind - used to create a new set of data describing typical daily driving distances, dwell durations, and normalized electric vehicle depot charging load curves for MHDVs. The dataset reflects the subset of MHDV operating patterns that may originate from a consistent depot each day and rely on the same depot for charging. In addition to trucks with depot-centric vocational patterns, the data describes operations of transit buses and school buses, each with a depot-centric focus. The dataset is available to the public and suitable for national analysis. It can inform research, infrastructure planning, and policymaking regarding the electrification of MHDVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics

Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. Lastly, this paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples

The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

NuSTAR SPECTROSCOPY OF MULTI-COMPONENT X-RAY REFLECTION FROM NGC 1068

We report on high-energy X-ray observations of the Compton-thick Seyfert 2 galaxy NGC 1068 with NuSTAR, which provide the best constraints to date on its >10 keV spectral shape. The NuSTAR data are consistent with those from past and current instruments to within cross-calibration uncertainties, and we find no strong continuum or line variability over the past two decades, which is in line with its X-ray classification as a reflection-dominated Compton-thick active galactic nucleus. The combined NuSTAR, Chandra, XMM-Newton, and Swift BAT spectral data set offers new insights into the complex secondary emission seen instead of the completely obscured transmitted nuclear continuum. The critical combination of the high signal-to-noise NuSTAR data and the decomposition of the nuclear and extranuclear emission with Chandra allow us to break several model degeneracies and greatly aid physical interpretation. When modeled as a monolithic (i.e., a single N{sub H}) reflector, none of the common Compton reflection models are able to match the neutral fluorescence lines and broad spectral shape of the Compton reflection hump without requiring unrealistic physical parameters (e.g., large Fe overabundances, inconsistent viewing angles, or poor fits to the spatially resolved spectra). A multi-component reflector with three distinct column densities (e.g., with best-fit values of N{sub H} of 1.4 × 10{sup 23}, 5.0 × 10{sup 24}, and 10{sup 25} cm{sup −2}) provides a more reasonable fit to the spectral lines and Compton hump, with near-solar Fe abundances. In this model, the higher N{sub H} component provides the bulk of the flux to the Compton hump, while the lower N{sub H} component produces much of the line emission, effectively decoupling two key features of Compton reflection. We find that ≈30% of the neutral Fe Kα line flux arises from >2″ (≈140 pc) and is clearly extended, implying that a significant fraction (and perhaps most) of the <10 keV reflected component arises from regions well outside a parsec-scale torus. These results likely have ramifications for the interpretation of Compton-thick spectra from observations with poorer signal-to-noise and/or more distant objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

CONSTRAINTS ON THE NEUTRON STAR AND INNER ACCRETION FLOW IN SERPENS X-1 USING NuSTAR

We report on an observation of the neutron star low-mass X-ray binary Serpens X-1, made with NuSTAR. The extraordinary sensitivity afforded by NuSTAR facilitated the detection of a clear, robust, relativistic Fe K emission line from the inner disk. A relativistic profile is required over a single Gaussian line from any charge state of Fe at the 5σ level of confidence, and any two Gaussians of equal width at the same confidence. The Compton back-scattering ''hump'' peaking in the 10-20 keV band is detected for the first time in a neutron star X-ray binary. Fits with relativistically blurred disk reflection models suggest that the disk likely extends close to the innermost stable circular orbit (ISCO) or stellar surface. The best-fit blurred reflection models constrain the gravitational redshift from the stellar surface to be z {sub NS} ≥ 0.16. The data are broadly compatible with the disk extending to the ISCO; in that case, z {sub NS} ≥ 0.22 and R {sub NS} ≤ 12.6 km (assuming M {sub NS} = 1.4 M {sub ☉} and a = 0, where a = cJ/GM {sup 2}). If the star is as large or larger than its ISCO, or if the effective reflecting disk leaks across the ISCO to the surface, the redshift constraints become measurements. We discuss our results in the context of efforts to measure fundamental properties of neutron stars, and models for accretion onto compact objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Band gap and refractive index tunability in thallium based layered mixed crystals

Compositional variation of the band gap energy and refractive index of TlMeX{sub 2}-type (Me = Ga or In and X = S or Se) layered mixed crystals have been studied by the transmission and reflection measurements in the wavelength range of 400–1100 nm. The analysis of absorption data of TlGa{sub 1-x}In{sub x}Se{sub 2}, TlGa(S{sub 1−x}Se{sub x}){sub 2}, TlGa{sub 1−x}In{sub x}S{sub 2}, and TlIn(Se{sub 1−x}S{sub x}){sub 2} mixed crystals revealed the presence of both optical indirect and direct transitions. It was found that the energy band gaps of mixed crystals decrease at the replacing of gallium atoms by indium and of sulfur atoms by selenium ones. Through the similar replacing of atoms (smaller atoms by larger ones) in the studied mixed crystals, the refractive index shows the quite opposite behavior.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

The photosynthetic capacity or the CO 2 -saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C 4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that V max can be well predicted from spectra (r=0.81). Here, the integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of V max (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to V max prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Assessing the Effectiveness of Large-Scale Environmental Restoration: Challenges and Opportunities

A recent National Academies consensus report addresses monitoring and assessment of cumulative effects of large-scale and multiple restoration projects within the context of long-term environmental change. Fines and penalties from the Deepwater Horizon oil spill in the Gulf of Mexico (GoM) have supported hundreds of restoration projects at spatial scales not often possible in the past. Here, in this paper, the report committee members and staff provide personal reflections from our time working on the study. We found that gaps in data collection, issues with data accessibility, and a lack of synthesis and analysis are hindering the ability to answer a basic question: What are the impacts of these many restoration efforts on improving ecosystem health and productivity in the GoM at the regional and Gulf wide scale? Restoration efforts are occurring in environments where many trends are changing and exhibiting higher variability than in the past, suggesting that previously successful restoration practices may no longer be adequate to compensate for the effects of environmental changes and variability. Our proposed approach to these challenges includes employing emerging monitoring technologies; using conceptual models; devising an adaptive management framework; rethinking restoration outcome goals; assessing cumulative effects; and undertaking rigorous synthesis and analysis of existing information on long-term environmental trends and restoration efforts. Restoration scientists and practitioners working in the GoM have an unprecedented opportunity to demonstrate large-scale environmental recovery if advances in monitoring, synthesis, assessment, and action are taken quickly. We are cautiously optimistic that, with mid-course adjustments, continued progress toward large-scale environmental recovery is possible.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Agentic traffic intelligence: Augmented human-in-the-loop scenario generation for microscopic traffic simulation

Traditional microscopic traffic simulation generation often relies on static datasets and manual design, limiting its ability to simulate complex conditions easily. This paper presents a novel framework, Agentic Traffic Intelligence, which combines human approval large language models (LLMs), the Real-Twin tool, and multi-agent systems to perform realistic microscopic traffic simulation scenario generation. The proposed framework incorporates human-in-the-loop (HIL) control, retrieval-augmented generation (RAG), and multi-agent control mechanisms. HIL mechanisms are used to guide multiple LLMs focused on attributes for microscopic simulation generation and to improve the interpretability and transparency of LLM execution for users. RAG enhances context extraction by dynamically integrating external knowledge sources for traffic scenario generation foundations. A multi-agent architecture with supervisory control coordinates the interaction of simulation components, including traffic simulators, control logic, and calibration tools. This enables the synthesis of simulation-ready scenarios that reflect dynamic demand profiles and behavior controls. Furthermore, the framework fuses multisource traffic data with unstructured context and supports iterative refinement through interactive user feedback. Validated through microscopic simulation using Simulation of Urban Mobility, the generated scenarios demonstrate high-fidelity network generation with inflow and turn movement and behavioral calibration, offering a robust and efficient tool for stress-testing and optimizing urban mobility systems.

Hierarchical multi-agent control↗