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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Using Satellite‐Derived Fire Arrival Times for Coupled Wildfire‐Air Quality Simulations at Regional Scales of the 2020 California Wildfire Season

Abstract Wildfire frequency has increased in the Western US over recent decades, driven by climate change and a legacy of forest management practices. Consequently, human structures, health, and life are increasingly at risk due to wildfires. Furthermore, wildfire smoke presents a growing hazard for regional and national air quality. In response, many scientific tools have been developed to study and forecast wildfire behavior, or test interventions that may mitigate risk. In this study, we present a retrospective analysis of 1 month of the 2020 Northern California wildfire season, when many wildfires with varying environments and behavior impacted regional air quality. We simulated this period using a coupled numerical weather prediction model with online atmospheric chemistry, and compare two approaches to representing smoke emissions: an online fire spread model driven by remotely sensed fire arrival times and a biomass burning emissions inventory. First, we quantify the differences in smoke emissions and timing of fire activity, and characterize the subsequent impact on estimates of smoke emissions. Next, we compare the simulated smoke to surface observations and remotely sensed smoke; we find that despite differences in the simulated smoke surface concentrations, the two models achieve similar levels of accuracy. We present a detailed comparison between the performance and relative strengths of both approaches, and discuss potential refinements that could further improve future simulations of wildfire smoke. Finally, we characterize the interactions between smoke and meteorology during this event, and discuss the implications that increases in regional smoke may have on future meteorological conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Inferring and evaluating satellite-based constraints on NO x emissions estimates in air quality simulations

Satellite observations of tropospheric NO 2 columns can provide top-down observational constraints on emissions estimates of nitrogen oxides (NO x ). Mass-balance-based methods are often applied for this purpose but do not isolate near-surface emissions from those aloft, such as lightning emissions. Here, we introduce an inverse modeling framework that couples satellite chemical data assimilation to a chemical transport model. In the framework, satellite-constrained emissions totals are inferred using model simulations with and without data assimilation in the iterative finite-difference mass-balance method. The approach improves the finite-difference mass-balance inversion by isolating the near-surface emissions increment. We apply the framework to separately estimate lightning and anthropogenic NO x emissions over the Northern Hemisphere for 2019. Using overlapping observations from the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), we compare separate NO x emissions inferences from these satellite instruments, as well as the impacts of emissions changes on modeled NO 2 and O 3 . OMI inferences of anthropogenic emissions consistently lead to larger emissions than TROPOMI inferences, attributed to a low bias in TROPOMI NO 2 retrievals. Updated lightning NO x emissions from either satellite improve the chemical transport model's low tropospheric O 3 bias. The combined lighting and anthropogenic emissions updates improve the model's ability to reproduce measured ozone by adjusting natural, long-range, and local pollution contributions. Thus, the framework informs and supports the design of domestic and international control strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Trusted Simulation: Considering Model Quality in the Context of User Trust

A high‐quality simulation model should help its users to easily and appropriately calibrate their trust in the model. Traditional evaluation metrics such as validation and robustness are necessary but insufficient for this task. Trust calibration depends on factors like the model's transparency, applicability to intended use, usability, reputation, and consideration of potential bias. This article proposes a framework for designing and evaluating system dynamics models by considering factors that contribute to the proper calibration of user trust. This framework takes inspiration from trusted artificial intelligence, broadening our traditional concept of model quality and explicitly focusing on what users need to consider a model trustworthy and to understand the model's relevance to its intended purpose. The trusted simulation framework can improve our integration of model quality activities throughout the modeling process, leading to more impactful and better‐targeted model design, development, and evaluation.

Naugle, Asmeret Bier [Sandia National Laboratories↗
DOE OSTI2024

Comparing the interactions between particulate matter and cloud properties over two populated cities in Texas using WRF-Chem fine-resolution modeling

Accurate modeling of aerosol-cloud interactions is essential for reliable weather and air quality simulations, given their significant impact on precipitation patterns, cloud dynamics, and aerosol distributions. This study employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to examine the impact of enhanced meteorological simulations, achieved through advanced microphysics parameterization supported by data assimilation techniques, on air quality across Texas on August 19 and 20, 2022. We tested four distinct configurations: (1) the Morrison two-moment bulk microphysics scheme, (2) Morrison's with observation nudging, (3) the Spectral Bin Microphysics (SBM), and (4) SBM with observation nudging. While the SBM scheme is known for its detailed representation of aerosol-cloud interactions, our focus was on how improvements in meteorological accuracy translate to more precise air quality simulations. Our findings demonstrated a progressive improvement in simulation accuracy, starting with the Morrison's scheme and further enhanced by adopting the SBM scheme, complemented by incorporating observation nudging. Specifically, the combination of the SBM scheme and the nudging substantially enhanced the model's ability to capture convective precipitation events, as shown by better alignment with NEXRAD radar reflectivity, with R increasing from –0.21 to 0.82, IOA from 0.10 to 0.87, and NMB decreasing from 99% to 34% in Houston. The enhanced meteorology translated into more accurate PM 2.5 concentration simulations, particularly through the more accurate representation of aerosol washout during precipitation events. In Houston, the SBM scheme with nudging improved the model's PM 2.5 simulations significantly, with NMB decreasing from –20% to 5% and IOA improving from 0.43 to 0.61. In San Antonio, improvements were also notable, with NMB improved from –27% to –22%, R increased from 0.48 to 0.82, and IOA increased from 0.66 to 0.86. Furthermore, our results underscore the crucial role of accurate meteorological simulations in refining our understanding of aerosol behaviors in relation to precipitation patterns, directly enhancing the reliability and effectiveness of air quality modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Innovative approaches to neutron beam optimization: A case study of FP14 at LANSCE

This paper describes an experimental method to acquire high resolution energy- and spatially- resolved neutron beam spots using the time-gated neutron imaging system with Teledyne Pi-MAX4 camera. These experimental data offer a unique opportunity for benchmarking beam spot simulations. High-quality simulations depend significantly on a high-fidelity geometry model, which can be challenging for legacy facilities. We informed our MCNPX geometry model by latest metrology survey employing Leica laser tracker ATS600. It gave us a high fidelity description of our facility geometry. Such a robust integration of novel tools and methods yields a previously unattainable level of accuracy in both predicting and capturing neutron beam spots, marking a notable advancement over traditional methods reliant on static image plates. Here, to demonstrate the practical application of these tools, we are showing a non-uniform beam spot challenge at our Flight Path 14 (FP14) at the Los Alamos Neutron Science Center (LANSCE). Our precise MCNPX prediction of beam spot shifting as function of neutron energy was confirmed by experimental beam spot measured with extremely high level of detail. Results of this research demonstrate a significant leap in neutron beam optimization at LANSCE and set a new benchmark in beam spot characterization. The advanced methods presented here have potential for adoption at similar research facilities worldwide, aiming at substantial improvement in neutron beam delivery for experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Accuracy Enhancement of Nuclear Power Plant Simulators Utilizing High Accuracy Simulation Predictions

More recently, reactor core simulators for core designs associated with commercial nuclear power plants that utilize what is believed to be higher fidelity models have been developed. Features such as neutronics models that utilize transport equation solvers with fine spatial meshes and many energy-groups, thermal-hydraulic models that utilize sub-channel solvers with fine spatial mesh and capable of treating a wide range of fluid conditions, and fuel-coolant chemistry interaction models capable of treating CRUD deposition are to be found in these higher fidelity core simulators. These reactor core simulators require access to higher performance computers, characterized by many processors, cores and large memory. So associated with utilization of these simulators is access to high performance computers and ability to accommodate in one’s workflow longer execution times. By contrast, currently used core simulators by the nuclear industry can execute on engineering workstations and have execution times of seconds to minutes. The desirability for having short execution times is not only desired for support of time critical tasks but supports the mental process of decision making by engineers. The goal of the work reported upon here has the objective of retaining the fidelity of higher fidelity models while retaining the ability to utilize engineering workstations. Beyond the core simulator goal, additional goals of this work include incorporating the just described core simulator capability into a Nuclear Steam Supply System (NSSS) simulator, and to incorporate the resulting capability into an environment supportive of design and operational decision making associated with nuclear power stations. The model selected for the core neutronics model is the NESTLE code, for the core thermal-hydraulic model is the CTF code utilizing coarse mesh, and for the NSSS model is the RELAP5-3D code. WSC’s proprietary 3KEYMASTERTM platform is being used to provide software coupling, user interface, visualization, and reporting. The NESTLE core neutronics simulator was first integrated with the CTF core thermal-hydraulic simulator using CTF developed communication commands which are also used for CTF to communicate with RELAP5-3D under WSC’s proprietary 3KEYMASTERTM platform. To assure NESTLE prediction consistency with higher fidelity core neutronic simulators, buffer codes have been created to automatically generate from output files written by the VERA core simulator the NESTLE nodal neutronic parameter’ library, geometry, and pin-power reconstruction input files, thereby avoiding a number of challenges associated with utilizing lattice physics codes and providing consistency with VERA predictions. To treat absorber rod effects a multi-set library is utilized, where a set refers to a specific absorber rod fully inserted pattern. A coarse spatial mesh CTF model was developed with features added that support using CTF as envisioned in the engineering quality simulator. A hybrid meshing approach was implemented to allow for automated construction of models with mixed levels of refinement. Specifically, a core model could resolve some assemblies at a nodal level (4 subchannels per assembly) and others at a pin-resolution (one subchannel per coolant subchannel in the assembly). The intention is that this will allow for better resolution of limiting conditions such as DNBR and PCT, which are based on local rod and subchannel conditions. Further development was done of features that enhance the capabilities for the envisioned engineering quality simulator that has been developed, but now for RELAP-3D. The RELAP5-3D code development includes ability to model more than 999 components and the addition of the cross-channels turbulence mixing model and the void drift model that are implemented in CTF, aiming to achieve closer prediction agreement of the two codes for transient simulations, specifically, more accurate matches of the overall mass, momentum, and energy exchanges of both the liquid and gas phases between the neighboring core assemblies. Graphics were also developed for the Instructor Station for this project under WSC’s proprietary 3KEYMASTERTM platform to facilitate design and operational decision making.

42 ENGINEERING↗
DOE OSTI2022

Simulation process and data flow for a large system dynamics model

This paper documents the workflow and supporting technologies that a large system dynamics model, the biomass scenario model, employs to streamline the data preparation, simulation, quality control, and analysis process at the National Renewable Energy Laboratory. The workflow centers on automation of routine aspects of the flow of data between data stores, simulations, and visualizations. It enforces quality checks on data, reproducibility of computations, and traceability of results, while maintaining complete archives of modeling and analysis artifacts. The resulting frictionless simulation/analysis environment supports large-scale sensitivity analysis, interactive creation of ensembles of simulations, and rapid visualization-based exploration of simulation results.

09 BIOMASS FUELS↗
DOE OSTI2024

Differentiable Simulation of a Liquid Argon Time Projection Chamber

Liquid argon time projection chambers (LArTPCs) are widely used in particle detection for their tracking and calorimetric capabilities. The particle physics community actively builds and improves high-quality simulators for such detectors in order to develop physics analyses in a realistic setting. The ability of these simulators to mimic real, measured data is limited by the modeling of the physical detectors used for data collection. This modeling can be improved by performing dedicated calibration measurements. Conventional approaches calibrate individual detector parameters or processes one at a time. However, the impact of detector processes is entangled, making this a poor description of the underlying physics. We introduce a differentiable simulator that enables a gradient-based optimization, allowing for the first time a simultaneous calibration of all detector parameters. We describe the procedure of making a differentiable simulator, highlighting the challenges of retaining the physics quality of the standard, non-differentiable version while providing meaningful gradient information. We further discuss the advantages and drawbacks of using our differentiable simulator for calibration. Finally, we provide a starting point for extensions to our approach, including applications of the differentiable simulator to physics analysis pipelines.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Modeling fission product diffusion in TRISO fuel particles with BISON

Diffusion of fission products in intact TRISO particles depends on particle geometry, fission product source rates, time, temperature, and temperature-dependent diffusion coefficients. Simulating this diffusion process requires models for source rates and diffusion coefficients, plus computation of the temperature field if not prescribed. In addition, simulation quality depends on discretization of the geometry, appropriate time stepping, and the accuracy of the solution method. In this paper, we explore the simulation of fission product diffusion in TRISO fuel particles using the finite element method via the fuel performance code Bison. Recent material model development has occurred in Bison for each material present in tri-structural isotropic (TRISO) fuel particles: the buffer, inner pyrolytic carbon, silicon carbide, and outer pyrolytic carbon layers, as well as the fuel kernel. Also, new mesh generation and fission product release fraction capabilities have been added. Diffusion capabilities are shown to converge to the correct solution via formal verification tests. A large number of code benchmarking problems are also given, with good results, showing that Bison’s computed release fractions closely match those of other software tools. Finally, a significant validation effort is detailed in which fission product release, measured as part of the AGR-1 capsule experiments, is compared to Bison outputs. Bison outputs compare very well to the experimental data and to PARFUME results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Generative adversarial networks for scintillation signal simulation in EXO-200

Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network — a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. Furthermore, the network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Resolved photons in S HERPA

We present the first complete simulation framework, in the SHERPA event generator, for resolved photon interactions at next-to leading order accuracy. It includes photon spectra obtained through the equivalent-photon approximation, parton distribution functions to parametrize the hadronic structure of quasi-real photons, the matching of the parton shower to next-to leading order QCD calculations for resolved photon cross sections, and the modelling of multiple-parton interactions. We validate our framework against a wide range of photo-production data from LEP and HERPA experiments, observing good overall agreement. We identify important future steps relevant for high-quality simulations at the planned Electron–Ion Collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Modeling the Air Quality Impacts of Future Energy Scenarios

Many greenhouse gas (GHG) emission reduction measures achieve simultaneous reductions in air pollutants. Human-Earth system models can estimate such emission changes in the energy system but using them in chemistry-transport models (CTMs) to study their air quality impacts involves resource-intensive emissions processing. This is greatly simplified by an emissions scaling approach linking state-level emissions estimated by a human-Earth system model to a CTM. A scenario continuing pre-2022 energy policy in the U.S. to 2050 shows widespread air quality improvements over the 2015 baseline from SO 2 and NO x emission reductions of 50–80% from electricity generation and light-duty vehicles. Scenarios of GHG mitigation and vehicle electrification at the state and national level add further benefits. However, PM 2.5 increases from increased use of wood heating and bioenergy suggest that additional PM 2.5 management may be needed when using biofuels. In conclusion, this approach helps assess multiple future energy scenarios efficiently without sacrificing chemical detail in the air quality simulations.

air quality↗
DOE OSTI2021

Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Global River Topology (GRIT): A Bifurcating River Hydrography

Existing global river networks underpin a wide range of hydrological applications but do not represent channels with divergent river flows (bifurcations, multi‐threaded channels, canals), as these features defy the convergent flow assumption that elevation‐derived networks (e.g., HydroSHEDS, MERIT Hydro) are based on. Yet, bifurcations are important features of the global river drainage system, especially on large floodplains and river deltas, and are also often found in densely populated regions. Here we developed the first raster and vector‐based Global RIver Topology that not only represents the tributaries of the global drainage network but also the distributaries, including multi‐threaded rivers, canals and deltas. We achieve this by merging a 30 m Landsat‐based river mask with elevation‐generated streams to ensure a homogeneous drainage density outside of the river mask for rivers narrower than approximately 30 m. Crucially, we employ the new 30 m digital terrain model, FABDEM, based on TanDEM‐X, which shows greater accuracy over the traditionally used SRTM derivatives. After vectorization and pruning, directionality is assigned by a series of elevation, flow angle and continuity approaches. The new global network and its attributes are validated using gauging stations, comparison with existing networks, and randomized manual checks. The new network represents 19.6 million km of streams and rivers with drainage areas greater than 50 km 2 and includes 67,495 bifurcations. With the advent of hyper‐resolution modeling and artificial intelligence, GRIT is expected to greatly improve the accuracy of many river‐based applications such as flood forecasting, water availability and quality simulations, or riverine habitat mapping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Physics model enhancements in MARLEY version 2

Large next-generation neutrino detectors, such as those designed for accelerator-based oscillation experiments, are expected to have impressive sensitivity to MeV astrophysical neutrinos generated by the Sun and by supernovae. There are also numerous related prospects to search for MeV-scale phenomena beyond the Standard Model. In contrast to detectors constructed with water and hydrocarbon targets, which will be primarily sensitive to electron antineutrinos via inverse beta decay, the upcoming Deep Underground Neutrino Experiment (DUNE) will provide unparalleled capabilities to measure low-energy electron neutrinos from cosmic sources. Interpreting such future measurements in DUNE will require a high-quality simulation of MeV interaction physics in the argon nucleus. The MARLEY (Model of Argon Reaction Low Energy Yields) neutrino event generator has been designed with the goal of delivering the needed simulation capabilities for DUNE and other argon-based neutrino detectors. In this poster, I will present recent improvements to MARLEY's suite of interaction models, which will be released for general use by the neutrino community in version 2 of the software.

Gardiner, Steven James [Fermilab] (ORCID:000000028↗
DOE OSTI2026

Physics model enhancements in MARLEY version 2

Large next-generation neutrino detectors, such as those designed for accelerator-based oscillation experiments, are expected to have impressive sensitivity to MeV astrophysical neutrinos generated by the Sun and by supernovae. There are also numerous related prospects to search for MeV-scale phenomena beyond the Standard Model. In contrast to detectors constructed with water and hydrocarbon targets, which will be primarily sensitive to electron antineutrinos via inverse beta decay, the upcoming Deep Underground Neutrino Experiment (DUNE) will provide unparalleled capabilities to measure low-energy electron neutrinos from cosmic sources. Interpreting such future measurements in DUNE will require a high-quality simulation of MeV interaction physics in the argon nucleus. The MARLEY (Model of Argon Reaction Low Energy Yields) neutrino event generator has been designed with the goal of delivering the needed simulation capabilities for DUNE and other argon-based neutrino detectors. In this talk, I will present recent improvements to MARLEY's physics models, which will be released for general use by the neutrino community in version 2 of the software.

Gardiner, Steven James [Fermilab] (ORCID:000000028↗
DOE OSTI2021

On the Representation of Hyporheic Exchange in Models for Reactive Transport in Stream and River Corridors

Efforts to include more detailed representations of biogeochemical processes in basin-scale water quality simulation tools face the challenge of how to tractably represent mass exchange between the flowing channels of streams and rivers and biogeochemical hotspots in the hyporheic zones. Multiscale models that use relatively coarse representations of the channel network with subgrid models for mass exchange and reactions in the hyporheic zone have started to emerge to address that challenge. Two such multiscale models are considered here, one based on a stochastic Lagrangian travel time representation of advective pumping and one on multirate diffusive exchange. The two models are formally equivalent to well-established integrodifferential representations for transport of non-reacting tracers in steady stream flow, which have been very successful in reproducing stream tracer tests. Despite that equivalence, the two models are based on very different model structures and produce significantly different results in reactive transport. In a simple denitrification example, denitrification is two to three times greater for the advection-based model because the multirate diffusive model has direct connections between the stream channel and transient storage zones and an assumption of mixing in the transient storage zones that prevent oxygen levels from dropping to the point where denitrification can progress uninhibited. By contrast, the advection-based model produces distinct redox zonation, allowing for denitrification to proceed uninhibited on part of the hyporheic flowpaths. These results demonstrate that conservative tracer tests alone are inadequate for constraining representation of mass transfer in models for reactive transport in streams and rivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the ab initio level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. Here we show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗