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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

FuSED Users Manual, 5.24

The Fusion of Simulation, Experiment, and Data (FuSED) team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verification/validation analysis of systems. This document provides a user’s guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

FuSED – Users Manual – (V.5.26)

The Fusion of Simulation, Experiment, and Data (FuSED) team provides a set of tools for solving inverse problems in structural dynamics (InverseSD) and thermal physics (InverseAria), a sensor placement optimization tool via Optimal Experimental Design (OED), and a decision boundary tool using SVMs (TRACE). These methods are used for designing experiments, model calibration, and verification/validation analysis of systems. This document provides a user’s guide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Leveraging ARM Data to Improve Models for Predictive Understanding of Energy and Security Challenges

Extreme weather and natural hazards can disrupt the energy sector, affecting demand, generation, transmission, distribution, consumption and operational planning at regional and national scales. These disruptions stem from a broad range of atmospheric phenomena, including winter storms, freezing rain, wet snow loading, severe convection, flooding and landslides, wildfires, prolonged heat, and drought. Many of these same phenomena can also affect national security through impacts to transportation and infrastructure. To support the U.S. Department of Energy (DOE) focus on energy resilience and national security, the Atmospheric Radiation Measurement (ARM) User Facility is uniquely positioned to contribute measurement data, analyses, and modeling frameworks that can significantly improve predictive understanding of these hazards to mitigate their effects. To explore this opportunity, ARM convened a two-part virtual workshop in November 2025. The workshop engaged interdisciplinary experts in atmospheric science, energy systems, modeling, and operations. The goal of the meeting was to engage with these interdisciplinary experts to address three questions: • What are examples of atmospheric processes that represent significant risks to energy security or national security and where are those risks greatest? • What measurements or measurement strategies would improve ARM’s capacity to address these issues? • How can ARM and users of the ARM facility better work with the Energy Exascale Earth System Model (E3SM) and multi-sector modeling communities to apply ARM data to improving E3SM simulations of these phenomena? Participants were asked to submit white papers ahead of the meeting to initiate thinking on these themes and to help organize discussions. Workshop sessions were then organized around themes identified in the white papers. First from the white papers and then through subsequent discussions, workshop participants identified many examples that address the three questions listed above. Participants called out energy system vulnerabilities to weather phenomena such as the impact of freezing rain, strong winds, and excessive heat on power grids. They also noted the effects that weather phenomena could have on energy demand or supply (e.g., through effects of extreme temperatures). They called out security vulnerabilities such as impacts to crops from aerosol-borne pathogens and risks to industry due to melting permafrost in the Arctic. In all, over a dozen meteorological phenomena were linked to energy or security vulnerabilities. For many of the identified phenomena, participants pointed out where ARM was well poised to address issues (e.g., through measurements of cloud microphysics to inform studies of freezing rain) but also noted needs for additional measurements or modified measurement strategies. For example, adaptive scanning of severe weather would be valuable for probing winter storms or severe convection. Participants pointed out the value in integrating external observations with ARM measurements and with applying artificial intelligence (AI) to ARM observation analysis and they advocated for using model simulations to help optimize measurement strategies through Observing System Simulation Experiments (OSSEs). It was clear from the workshop that there are many ways that ARM observations can be used to mitigate energy and security concerns, but meeting participants were also asked to identify what they considered to be the greatest opportunities by ranking issues pertaining to the three workshop questions. This was accomplished through a survey administered to participants between the two virtual sessions. The highest-priority phenomena identified were winter storms, severe convection, and arctic processes. Discussion in the second session, therefore, focused primarily on these three areas, which were most fully developed in exploring ARM opportunities. Nevertheless, it was also clear that ARM has opportunities to contribute to all the identified topics. This report describes the workshop, including input from discussion and white papers (Sections 2 and 3) and a list of priority recommendations (section 4). Many other ideas for ARM contributions are discussed in individual white papers (Appendix D).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Assessing the limitations of commercial sensors and models for supporting marine carbon dioxide removal monitoring: a case study

Several unknowns remain surrounding marine Carbon Dioxide Removal (mCDR) monitoring, reporting, and verification (MRV) practices and capabilities. Current in-situ sensor technology is limited (primarily pH and pCO 2 ), requiring calculations and assumptions to estimate changes in carbonate chemistry parameters, including total alkalinity (TA). Considering that cost, energy consumption, and accuracy of commercial sensors can vary by orders of magnitude, understanding how well existing sensors perform in an mCDR context is important for this emerging community. Likewise, documenting sensor limitations and how relatively simple models can optimize sensor deployments will improve MRV efforts and support protocol development. Here we (1) compare performance a variety of commercially available sensors in a blind mesocosm experiment simulating ocean alkalinity enhancement (OAE), and how sensor performance impacted carbonate chemistry estimates; (2) evaluate if sensors can distinguish the OAE signal from natural variability during a small scale OAE field test in Sequim Bay, WA, USA, and (3) use an idealized ocean biogeochemistry model to explore optimal sensor network design based on (1) and (2). Our mesocosm results indicate that correctly constraining pH uncertainty will be critical for accurate TA estimates with current sensor technology compared to the less impactful variation caused by uncertainty in pCO 2 (pH data that are presented throughout are reported on the total scale (pH T ) unless otherwise noted). Our pilot field test demonstrated that sensors were capable of distinguishing mCDR signatures from natural variability under optimal real-world conditions. Idealized modeling simulations of the field test showed that a range of sparse and dense (3 to 100) sensors sampling areas of detectable increases will underestimate the net change in surface pH by at least 35–55%, at both realistic and highly elevated alkalinity input levels. We also highlight the limitations of current sensing technology for MRV, and the importance of ocean biogeochemistry models as critical tools for predicting when and where mCDR signals will be detectable using available sensors. Overall, our findings suggest that commercially available pCO 2 sensors and some pH sensors will form an important backbone for mCDR MRV tasks, though complete MRV characterization will require these data to be used in combination with other tools.

OAE↗
DOE OSTI2020

The Impact of Surficial Biochar Treatment on Acute H2S Emissions during Swine Manure Agitation before Pump-Out: Proof-of-the-Concept

Acute releases of hydrogen sulfide (H2S) are of serious concern in agriculture, especially when farmers agitate manure to empty storage pits before land application. Agitation can cause the release of dangerously high H2S concentrations, resulting in human and animal fatalities. To date, there is no proven technology to mitigate these short-term releases of toxic gas from manure. In our previous research, we have shown that biochar, a highly porous carbonaceous material, can float on manure and mitigate gaseous emissions over extended periods (days–weeks). In this research, we aim to test the hypothesis that biochar can mitigate H2S emissions over short periods (minutes–hours) during and shortly after manure agitation. The objective was to conduct proof-of-the-concept experiments simulating the treatment of agitated manure. Two biochars, highly alkaline and porous (HAP, pH 9.2) made from corn stover and red oak (RO, pH 7.5), were tested. Three scenarios (setups): Control (no biochar), 6 mm, and 12 mm thick layers of biochar were surficially-applied to the manure. Each setup experienced 3 min of manure agitation. Real-time concentrations of H2S were measured immediately before, during, and after agitation until the concentration returned to the initial state. The results were compared with those of the Control using the following three metrics: (1) the maximum (peak) flux, (2) total emission from the start of agitation until the concentration stabilized, and (3) the total emission during the 3 min of agitation. The Gompertz’s model for determination of the cumulative H2S emission kinetics was developed. Here, 12 mm HAP biochar treatment reduced the peak (1) by 42.5% (p = 0.125), reduced overall total emission (2) by 17.9% (p = 0.290), and significantly reduced the total emission during 3 min agitation (3) by 70.4%. Further, 6 mm HAP treatment reduced the peak (1) by 60.6%, and significantly reduced overall (2) and 3 min agitation’s (3) total emission by 64.4% and 66.6%, respectively. Moreover, 12 mm RO biochar treatment reduced the peak (1) by 23.6%, and significantly reduced overall (2) and 3 min total (3) emission by 39.3% and 62.4%, respectively. Finally, 6 mm RO treatment significantly reduced the peak (1) by 63%, overall total emission (2) by 84.7%, and total emission during 3 min agitation (3) by 67.4%. Biochar treatments have the potential to reduce the risk of inhalation exposure to H2S. Both 6 and 12 mm biochar treatments reduced the peak H2S concentrations below the General Industrial Peak Limit (OSHA PEL, 50 ppm). The 6 mm biochar treatments reduced the H2S concentrations below the General Industry Ceiling Limit (OSHA PEL, 20 ppm). Research scaling up to larger manure volumes and longer agitation is warranted.

Chen, Baitong (ORCID:0000000266071253)↗
DOE OSTI2025

A Fast Framework for Generating Radioactive Mixture Spectra and Its Application to Remote High-Performance Mixture Identification

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

GADRAS↗
DOE OSTI2023

Medium-Range Order Resists Deformation in Metallic Liquids and Glasses

In crystals, lattice defects, such as dislocations, control mechanical deformation. Similarly, it is widely believed that even in glasses and liquids some kinds of defects, strongly disordered regions, play a major role in deformation. To identify defects researchers focused on the nature of the short-range order (SRO) in the nearest neighbor cage of atoms. However, recent results by experiment, simulation and theory raise serious questions about this assumption. They suggest that the atomic medium-range order (MRO) provides resistance against flow at the atomic level. Because the MRO is a bulk property, it implies that defects play only a limited role. This new insight is supported by the density wave theory which shows that the MRO is driven by a top-down global force, rather than being a consequence of the SRO in the bottom-up manner, and the MRO provides stiffness to resist deformation. We briefly summarize the density wave theory, show that the MRO is related to ductility of metallic glasses, and discuss the implications on the role of the MRO in the atomic-level mechanism of deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

On the Viability of Video Imaging in Leak Rate Quantification: A Theoretical Error Analysis

Optical gas imaging through multispectral cameras is a promising technique for mitigation of methane emissions through localization and quantification of emissions sources. While more advanced cameras developed in recent years have led to lower uncertainties in measuring gas concentrations, a systematic analysis of the uncertainties associated with leak rate estimation have been overlooked. We present a systematic categorization of the involved uncertainties with a focus on a theoretical analysis of projection uncertainties that are inherent to this technique. The projection uncertainties are then quantified using Large Eddy Simulation experiments of a point source release into the atmosphere. Our results show that while projection uncertainties are typically about 5% of the emission rate, low acquisition times and observation of the gas plume at small distances from the emission source (<10 m) can amount to errors of about 20%. Further, we found that acquisition times on the order of tens of seconds are sufficient to significantly reduce (>50%) the projection uncertainties. These findings suggest robust procedures on how to reduce projection uncertainties, however, a balance between other sources of uncertainty due to operational conditions and the employed instrumentation are required to outline more practical guidelines.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

CV4Quantum: Reducing the Sampling Overhead in Probabilistic Error Cancellation Using Control Variates

Quasiprobabilistic decompositions (QPDs) play a key role in maximizing the utility of near-term quantum hardware. For example, Probabilistic Error Cancellation (PEC) (an error mitigation technique) and circuit cutting (which enables large quantum computations to be performed on quantum hardware with a limited number of qubits) both involve QPDs. Computations based on QPDs typically incur large sampling overheads that grow exponentially with the number of error-terms mitigated or number of circuit-cuts employed, limiting their practical feasibility. In this work, we adapt the control variates variance reduction technique from the statistics literature in order to reduce the sampling overhead in QPD-based computations. We demonstrate our method using simulation experiments that mimic a realistic PEC scenario. In our experiments, we observed a more than 50% reduction in the number of samples needed to achieve a given precision, in more than 50% of the PEC-based estimations performed in the study when using our approach. We discuss how future research on constructing good control variates can lead to even stronger sampling overhead reduction.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Roadmap for warm dense matter physics

This roadmap presents the state-of-the-art, current challenges and near future developments anticipated in the thriving field of warm dense matter physics. Originating from strongly coupled plasma physics, high pressure physics and high energy density science, the warm dense matter physics community has recently taken a giant leap forward. This is due to spectacular developments in laser technology, diagnostic capabilities, and computer simulation techniques. Only in the last decade has it become possible to perform accurate enough simulations \& experiments to truly verify theoretical results as well as to reliably design experiments based on predictions. Consequently, this roadmap discusses recent developments and contemporary challenges that are faced by theoretical methods, and experimental techniques needed to create and diagnose warm dense matter. A large part of this roadmap is dedicated to specific warm dense matter systems and applications in astrophysics, inertial confinement fusion and novel material synthesis.

FOS: Physical sciences↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2020

The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory

Ground-based observatories use multi-sensor observations to characterize cloud and precipitation properties. One of the challenges is how to design strategies to best use these observations to understand these properties and evaluate weather and climate models. This paper introduces the Cloud resolving model Radar SIMulator (CR-SIM), which uses output from high-resolution cloud resolving models (CRMs) to emulate multi-wavelength, zenith-pointing, and scanning radar observables and multi-sensor (radar and lidar) products. CR-SIM allows direct comparison between an atmospheric model simulation and remote-sensing products using a forward-modeling framework consistent with the microphysical assumptions used in the atmospheric model. CR-SIM has the flexibility to easily incorporate additional microphysical modules, such as microphysical schemes and scattering calculations, and expand the applications to simulate multi-sensor retrieval products. In this paper, we present several applications of CR-SIM for evaluating the representativeness of cloud microphysics and dynamics in a CRM, quantifying uncertainties in radar-lidar integrated cloud products and multi-Doppler wind retrievals, and optimizing radar sampling strategy using observing system simulation experiments. These applications demonstrate the application of CR-SIM as a virtual observatory operator on high-resolution model output for a consistent comparison between model results and observations to aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements. CR-SIM is licensed under the GNU GPL package and both the software and the user guide are publicly available to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

PSTN-051: Survey Strategy and Cadence Choices for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST)

A summary of survey strategy and cadence choices, simulated and evaluated by the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Scheduler Team, prepared for the Survey Cadence and Optimization Committee (SCOC). A large telescope survey, covering the entire visible sky repeatedly every few days in multiple bandpasses over the course of ten years, is the core idea of the LSST. An area of about 20,000 square degrees observed under a wide range of conditions to deep coadded limiting magnitudes in bandpasses ugrizy enables cosmological studies and studies of the Milky Way structure with unprecedented precision; the same survey, when cadenced well, can serve to open new windows into our understanding of transient and variable stars, and extend our knowledge of small bodies throughout the Solar System by orders of magnitude. The outlines of, and some basic necessary requirements for these goals are outlined in the LSST Science Requirements Document (SRD). Finding options for the observing strategy to meet more detailed needs of an even wider range of science goals, as well as building the LSST Scheduler and Metrics Analysis Framework, has been the work of the LSST Scheduler Team with support and input from the astronomical community, including the Community Observing Strategy Evaluation Paper (COSEP), the Call for White Papers, numerous performance metrics, and guidance from the LSST Science Advisory Committee in their Recommendations for Operations Simulator Experiments. This report will help enable the SCOC to make its recommendation for the preferred survey strategy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A Data-Fusion Method using Bayesian Approach to Enhance Raw Data Accuracy of Position and Distance Measurements for Connected Vehicles

Accurate positioning of vehicles is a critical element of autonomous and connected vehicle systems. Most of other studies heavily focused on enhancing simultaneous localization and mapping (SLAM) methods, i.e., constructing or updating a map of an unknown environment and tracking an object within the map. This paper provides a method that can, in addition to existing SLAM or relevant methods, enhance the raw measurements of position and distance. The basic idea of this study is to identify and update the error distribution of each data source by combining all available information. A Bayesian approach was incorporated to estimate and update the error distribution of individual data sources or sensors. The proposed method can be conducted in real-time environments, and a self-learning scheme determines whether enough data has been collected to further improve the accuracy of such measurements. The simulated experiments show that the proposed model noticeably improves the accuracy of position and distance measurements. Especially, the estimated biases of position coordinates and distance measures are very close to the biases of true error distributions, with the R-squared over 0.98. A similar approach can also be utilized to enhance accuracy of other sensors or measurements in connected vehicle or relevant systems, where multi-data sources are available.

Lim, Hyeonsup↗
DOE OSTI2021

Strategies for Working Remotely: Responding to Pandemic-Driven Change.

In response to the COVID-19 pandemic, the Exascale Computing Project’s (ECP) Interoperable Design of Extreme-scale Application Software (IDEAS) productivity team launched the panel series Strategies for Working Remotely to facilitate informal, cross-organizational dialog in the absence of face-to-face meetings. In a time of pandemic, organizations increasingly need to reach across perceived boundaries to learn from each other, so that we can move beyond stand-alone silos to more connected multidisciplinary and multiorganizational configurations. The present paper argues that the unplanned transition to remote work, overuse of electronic communication, and need to unlearn habits associated with an overreliance on face-to-face, created unique opportunities to learn from the situation and accelerate cross-institutional cooperation and collaboration through online community dialog facilitated by informal panel discussions. Recommendations for facilitating online panel discussions to foster cross-organizational dialog are provided by applying the Simulation Experience Design Method.

Raybourn, Elaine M.↗
DOE OSTI2022

Feasibility Study of Real-Time Carbon Emission Responsive Electric Vehicle Charging Control in Buildings: Preprint

With the progressing electrification of the transportation sector, the source of carbon emissions is gradually shifting from fossil fuel to grid electricity because of electric vehicles (EVs). The carbon intensity of the grid can fluctuate significantly within hours due to the time-varying power generation mix. Therefore, shifting EV charging loads to cleaner hours in response to the carbon intensity signals can reduce carbon emissions. Existing EV charging control methods typically consider the electricity price or the available generation by distributed energy resources (e.g., photovoltaics) to inform decision-making. Such methods tend to reduce energy costs but may neglect the environmental impact of EV charging activities. We propose and compare four carbon emission responsive EV charging controllers with various control rules. The proposed controllers are evaluated based on simulation experiments using metrics such as carbon emission reduction potential, state of charge (SOC) at departure, and peak demand. We found that the need of EV owners to have full batteries at departure could lead to an emission increase when the curtailed EV charge was compensated before departure. Further, up to 12.7% of carbon emission reduction can be achieved if the EV owners reduce the target SOC at departure by less than 15%.

carbon emission↗
DOE OSTI2023

Validation of Hadron Mass Correction Schemes in Deep Inelastic Scattering at Low Energy Transfer

One method of describing the quark-gluon of the structure proton relies on factorizing its cross-section into a process-dependent short distance element and an independent long distance element that quantifies the quark and gluon momentum distributions within the proton. This research focuses on validating the kinematic range at which factorization theorems still accurately describe the cross-section, in particular for events that simulate scattering on large momentum quarks, that are sensitive to the details of the color mechanism. In this regime, effects which otherwise scale away at large energies, become non-negligible at low energies. The goal of this research is to assess the viability of an extended version of ?collinear factorization? recently proposed by J. Guerrero and A. Accardi, which includes kinematic corrections at quark level, known as ?hadron mass corrections?, to account for the masses of the quarks and the target. To accomplish this, deep inelastic scattering events are simulated using a realistic model of deep inelastic scattering, in which one may analytically calculate both the structure functions and the quark distribution functions. These simulated experiments are then used to test the viability of the proposed factorization theorem by fitting the quark distributions and comparing these to the analytically calculable ones. It is found that the viability of hadron mass corrections in low energy scattering experiments, such as those at Jefferson Lab, extends beyond the kinematic range suggested by analytic calculations alone, provided that residual higher twist corrections are simultaneously fitted to the data.

Krause, Andrew↗
DOE OSTI2024

Enabling Scale-Up Through Multi-Fidelity Adaptive Computing

We present ideas from our ongoing work in adaptive computing - an optimization framework that allows us to strategically deploy various fidelity level experiments and simulations to guide decision making. The framework aims to enable uncertainty quantified scale-up of simulations and experiments, which causes increased complexity. A key feature of the framework is the integration of user-specified local model trustworthiness estimates. Adaptive sampling strategies allow us to optimally exploit the multiple fidelity level information and trustworthiness measures to arrive at the best decisions within a highly limited budget of objective function evaluations.

adaptive sampling↗