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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

ProtoDUNE-VD for Beyond the Standard Model Searches: Initial Studies and Future Prospects

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino program designed to address fundamental questions in neutrino and astroparticle physics. ProtoDUNE, operating at the CERN Neutrino Platform, serves as a full-scale prototype for the DUNE Far Detector. In particular, the ProtoDUNE Vertical Drift (ProtoDUNE-VD) detector provides a powerful testbed for validating reconstruction and event selection techniques for future DUNE operations. In addition to detector R&D, ProtoDUNE enables a novel parasitic beam-dump search for beyond-the-Standard-Model (BSM) particles. However, it faces several challenges. Most notably, the ProtoDUNE-VD modules operate on the surface and are consequently exposed to an intense flux of cosmic rays, which requires a dedicated trigger. In addition, standard neutrinos are also produced in the T2 target area from the decay of unstable mesons, constituting a relevant background, which needs to be well understood and characterized a priori. We present the first studies based on 2025 data taken with a trigger designed to identify neutrino candidates at ProtoDUNE-VD. ProtoDUNE-VD’s high-resolution LArTPC imaging allows detailed reconstruction of decay and scattering signatures. This work demonstrates the complementarity of traditional tools such as Pandora and modern machine-learning approaches, providing key input for atmospheric neutrino and rare-event searches in the DUNE Vertical Drift program.

Bagdu, Halit [U. Iowa, Iowa City]↗
DOE OSTI2021

Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery

Remote sensing in combination with deep learning has become instrumental for efficiently and accurately classifying land-use and land-cover across large geographic areas. These technologies have also been successful in characterizing urban environments in terms of their structural units, structure types, or morphological regions. In these approaches, an urban area is partitioned into regions that exhibit homogeneous physical characteristics. However, existing approaches are typically limited to a single city, use inconsistent typologies, and lack scalability and generalization capacity. In this article, we propose an urban structural units categorization scheme and demonstrate its utility by applying it to 13 cities. Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities. These experiments offer insights into the strengths and limitations of deep neural networks for classifying urban structural units over diverse geographic regions and on heterogeneous collections of satellite imagery. The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines. Our validation on five cities shows that better performance is achieved with deep neural networks. Additionally, we evaluate the impact of input size, model depth, and spatial pyramid pooling to assess the generalization capacity of deep neural networks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

W-SMART Phase-I Pathway Analysis: Case Study - City of Boston, MA

The purpose of this study is to synthesize stakeholder and research learnings to date by exercising PNNL’s Waste - Sustainability Monitoring of Alternative Reuse Options over Time (W-SMART) sustainability protocol for the Greater Boston region. This report serves as a foundation for future discussion and project work to characterize the costs, risks, impacts, tradeoffs, and highest uses for major waste streams. This analysis differs from previous work by 1) incorporating results of a newly completed detailed resource assessment for the Greater Boston area; (2) providing a head-to-head pathway comparison without any policy supports (e.g., carbon or energy credits); and (3) focusing on locally relevant critical waste streams and reuse strategies, by assessing the cost-effectiveness of two complimentary pathways, including (a) expanded incineration of municipal solid waste (MSW) at existing treatment sites to produce baseload electricity, and (b) the conversion of blended municipal wastewater solids (i.e., sludge) and non-residential food waste to produce liquid transportation biofuels at a proposed hydrothermal liquefaction facility in Quincy, MA. The performance of each pathway is also compared to assumed business-as-usual waste management practices as a baseline.

09 BIOMASS FUELS↗
NASA NTRS2026

Mechanical Properties of Carbon Fiber Reinforced Composites Exposed to Cryogenic Conditions and Space Radiation via Simulation and Testing

As NASA missions extend beyond low Earth orbit, increasing reliance is placed on carbon fiber reinforced polymer (CFRP) composites for spacecraft structures where mass efficiency, durability, and long-term reliability are critical. In service, these materials are subjected to a combination of ultraviolet radiation, vacuum, ionizing radiation, atomic oxygen, and extreme thermal excursions under sustained mechanical loading. Flight systems such as the Boeing Starliner and SpaceX Dragon employ external composite structures that will experience these environments for extended durations. Although prior spaceflight and ground studies have reported limited changes in bulk mechanical properties, the synergistic effects of these environments on composite microstructure, particularly at the fiber matrix interphase, remain insufficiently characterized and represent a potential qualification and reliability risk. This study investigates the effects of short-term cryogenic exposure on a radiation shielding carbon epoxy composite, SC2020, as a ground-based analog for space relevant thermal extremes. The SC2020 material system has previously flown on the International Space Station under the Materials International Space Station Experiment (MISSE) program. Composite specimens were exposed to liquid nitrogen for 6 and 24 hours and evaluated using a multiscale characterization framework that combined ASTM D3039 tensile testing, Atomic Force Microscopy (AFM) based interphase analysis, and helium gas permeability measurements. Tensile testing showed no statistically significant or permanent degradation in global strength or modulus following cryogenic exposure. In contrast, AFM measurements revealed reductions in interphase modulus, weakened adhesion, and increased nanoscale heterogeneity, indicating localized degradation mechanisms not captured by conventional bulk testing. Gas permeability measurements showed a progressive increase in helium diffusion with exposure duration, consistent with micro-void formation or partial interfacial debonding. The results indicate that cryogenic exposure initiates degradation at the fiber matrix interphase while leaving global mechanical properties largely unchanged over short durations. These findings underscore the importance of multiscale diagnostics for identifying early-stage damage mechanisms that may influence long term performance and qualification margins for spaceflight composite structures. The data presented establish a cryogenic baseline for comparison with forthcoming MISSE flight exposure results and support ongoing NASA Established Program to Stimulate Competitive Research (EPSCoR) efforts aimed at improving composite qualification methodologies, risk assessment, and reliability prediction for space environments.

composite reliability↗
DOE OSTI2022

Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network.

Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machine learning approaches that learn parameters of a known partial differential equation (PDE) for a single instance of the input parameters at a fixed resolution, neural operators approximate the solution map of a family of PDEs [6, 7]. Despite their success, the uses of neural operators are so far restricted to relatively shallow neural networks and confined to learning hidden governing laws. In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images. Our NKN stems from the interpretation of the neural network as a discrete nonlocal diffusion reaction equation that, in the limit of infinite layers, is equivalent to a parabolic nonlocal equation, whose stability is analyzed via nonlocal vector calculus. The resemblance with integral forms of neural operators allows NKNs to capture long-range dependencies in the feature space, while the continuous treatment of node-to-node interactions makes NKNs resolution independent. The resemblance with neural ODEs, reinterpreted in a nonlocal sense, and the stable network dynamics between layers allow for generalization of NKN’s optimal parameters from shallow to deep networks. This fact enables the use of shallow-to-deep initialization techniques [8]. Our tests show that NKNs outperform baseline methods in both learning governing equations and image classification tasks and generalize well to different resolutions and depths.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2009

The Orbiting Wide-Angle Light Collectors (OWL) Mission for Charged-Particle Astronomy

The space-based OWL mission is designed to perform high-statistics measurements of ultra-high-energy cosmic rays (UHECR) using the Earth's atmosphere as a vast particle calorimeter, furthering the field of charged-particle astronomy. OWL has been developed in formal NASA instrument and mission studies and is comprised of two large telescopes separated by approx.600 km in 1000 km, near-equatorial orbits to stereoscopically image the near-UV air fluorescence emitted by UHECR-induced particle cascades. The High Resolution Fly's Eye (HiRes) Collaboration, and subsequently the Pierre Auger Observatory, recently reported confirmation of the expected Greisen-Zatsepin-Kuzmin (GZK) suppression of the UHECR flux above a few times 10(exp 19) eV. This observation is consistent with the majority of UHECR originating in astrophysical objects and reduces the need to invoke exotic physical processes. Particles observed above the GZK threshold energy must have come from sources within about 100 Mpc from the Earth. The small particle deflection angles expected at UHECR energies, with standard assumptions of extragalactic magnetic fields, are on the order of 1 degree. Thus by observing particles above the GZK threshold with sufficient exposure, there is the potential of identifying and characterizing individual UHECR sources. Auger has reported significant anisotropy in the arrival directions of UHECR at energies above about 6 10(exp 19) eV observed in the South, and a correlation to AGN in the 12th VCV catalog, suggesting that the sources of UHECR are traced by the distribution of luminous matter in the Universe. However, with similar statistics and the same event selection criteria, HiRes observations in the North are consistent with isotropy. Extended observations by Auger-South, by Telescope Array in the North, and possibly by the proposed Auger-North, will further these investigations. However, much greater exposures will be required to fully identify individual sources and measure their cosmic ray spectra. A five-year OWL mission would deliver approximately 10(exp 6)sq km/sr/yr of exposure with full aperture reached at approx. 10(exp 19) eV. The baseline 3 m optical aperture OWL telescopes with 45 degree full field-of-view are easily accomodated on a single conventional launch vehicle. On orbit, the simultaneous viewing of the same volume of atmosphere allows for precise stereo event reconstruction, which is nearly independent of the inclination of the particle track and tolerant of atmospheric conditions. The availability of monocular operation provides increased reliability or can be employed to increase the instantaneous aperture. In stereo mode, OWL is also capable of fully reconstructing horizontal or upward-moving showers and so has unmatched sensitivity to neutrino-induced events. The details of the OWL mission will he presented and its science capabilities will be discussed.

Mitchell, John↗
NASA NTRS2011

The First Intervention Study in Elder Self-Neglect: A Randomized Clinical Trial to Improve Vitamin D Levels

Despite high mortality rates, elder self-neglect is characterized by refusal of medical and social interventions. To date there have been no tested clinical interventions in elders who self-neglect. Previous research from the TEAM Institute has shown significantly low vitamin D levels in this population. This study aimed to determine the feasibility of a clinical intervention. Replacement of vitamin D was chosen because of its ease of administration and favorable safety profile. Methods: A randomized clinical trial using directly observed therapy of vitamin D was conducted using 50 elders, >65 years of age, with Adult Protective Services (APS) validated self-neglect. A staggered intervention with waiting controls was used to maximize statistical power. One-third (n=17) of the group was administered 50,000 IU vitamin D2 (ergocalciferol) monthly and the remainder (n=33) were administered 400 IU monthly. Serum 25-OH vitamin D was assessed at baseline and 5-months. Results: 69% agreed to participate in the study and of those n=40 (80%) remained at 5-months. At baseline, 12% (n=7) were deficient in vitamin D (<30nmol/L) and approximately 38% (n=22) had inadequate vitamin D levels (<50nmol/L). The baseline 25-OH vitamin D level was 59 nmol/L +25 (mean SD), and increased significantly to 72nmol/L +21 nmol/L at 5-months. Conclusion: These data are the first to provide evidence that clinical interventions are feasible in elders who self-neglect. The increase in vitamin D levels confirmed that the study personnel were able to successfully intervene community-dwelling elders with self-neglect. This study sets the precedent for future intervention and prevention studies

Burnett, Jason↗
NASA NTRS1991

Valsalva maneuver: Insights into baroreflex modulation of human sympathetic activity

Valsalva's maneuver, voluntary forced expiration against a closed glottis, is a well-characterized research tool, used to assess the integrity of human autonomic cardiovascular control. Valsalva straining provokes a stereotyped succession of alternating positive and negative arterial pressure and heart rate changes mediated in part by arterial baroreceptors. Arterial pressure changes result primarily from fluctuating levels of venous return to the heart and changes of sympathetic nerve activity. Muscle sympathetic activity was measured directly in nine volunteers to explore quantitatively the relation between arterial pressure and human sympathetic outflow during pressure transients provoked by controlled graded Valsalva maneuvers. Our results underscore several properties of sympathetic regulation during Valsalva straining. First, muscle sympathetic nerve activity changes as a mirror image of changes in arterial pressure. Second, the magnitude of sympathetic augmentation during Valsalva straining predicts phase 4 arterial pressure elevations. Third, post-Valsalva sympathetic inhibition persists beyond the return of arterial and right atrial pressures to baseline levels which reflects an alteration of the normal relation between arterial pressure and muscle sympathetic activity. Therefore, Valsalva straining may have some utility for investigating changes of reflex control of sympathetic activity after space flight; however, measurement of beat-to-beat arterial pressure is essential for this use. The utility of this technique in microgravity can not be determined from these data. Further investigations are necessary to determine whether these relations are affected by the expansion of intrathoracic blood volume associated with microgravity.

Smith, Michael L.↗
DOE OSTI2025

Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators

Abstract. Simulating low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1000–10 000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part 1, we construct an ensemble weather forecasting system based on spherical Fourier neural operators (SFNOs), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest-growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. With large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states during the rollout, and the ML ensemble passes a crucial spectral test in the literature. The IFS and ML ensembles have similar extreme forecast indices, and we show that the ML extreme weather forecasts are reliable and discriminating. These diagnostics ensure that the ensemble can reliably simulate the time evolution of the atmosphere, including low-likelihood high-impact extremes. In Part 2, we generate a huge ensemble initialized each day in summer 2023, and we characterize the simulations of extremes.

Mahesh, Ankur↗
DOE OSTI2025

Measurement of Multi-Proton Zero Pion Final States with the NuMI Neutrino Beam at ICARUS

Evidence suggests that charge-parity (CP) symmetry, or the principle that matter and antimatter must obey the same physics, might be violated in neutrino flavor mixing. The next-generation long-baseline experiment DUNE is designed to confirm whether or not neutrinos oscillate differently from antineutrinos. The magnitude of systematic uncertainties contributing to current long-baseline oscillation measurements, if left unimproved, is enough to jeopardize DUNE's ability to measure CP violation. Neutrino cross section measurements on argon targets are an essential input for improving neutrino interaction modelling and reducing the systematic uncertainties before DUNE turns on. The ICARUS experiment at Fermilab is well suited perform such measurements due to the NuMI neutrino beam providing neutrinos at the same energy as DUNE's first oscillation maximum. This work presents a measurement of muon neutrino charged-current interactions with multiple energetic protons and zero final state mesons in ICARUS. Multi-proton topologies are sensitive to contributions from two-particle two hole effects as well as resonant pion production and subsequent absorption via final state interactions (FSI) within the argon nucleus. Single-differential cross sections are measured in lab-frame opening angles and transverse kinematic imbalance variables, characterizing the initial sate momentum sharing between correlated nucleons and strength of FSI in argon.

Smedley, John [Rochester U.] (ORCID:00000002848611↗
NASA NTRS2005

Scanning Backscatter Lidar Observations for Characterizing 4-D Cloud and Aerosol Fields to Improve Radiative Transfer Parameterizations

Clouds have a powerful influence on atmospheric radiative transfer and hence are crucial to understanding and interpreting the exchange of radiation between the Earth's surface, the atmosphere, and space. Because clouds are highly variable in space, time and physical makeup, it is important to be able to observe them in three dimensions (3-D) with sufficient resolution that the data can be used to generate and validate parameterizations of cloud fields at the resolution scale of global climate models (GCMs). Simulation of photon transport in three dimensionally inhomogeneous cloud fields show that spatial inhomogeneities tend to decrease cloud reflection and absorption and increase direct and diffuse transmission, Therefore it is an important task to characterize cloud spatial structures in three dimensions on the scale of GCM grid elements. In order to validate cloud parameterizations that represent the ensemble, or mean and variance of cloud properties within a GCM grid element, measurements of the parameters must be obtained on a much finer scale so that the statistics on those measurements are truly representative. High spatial sampling resolution is required, on the order of 1 km or less. Since the radiation fields respond almost instantaneously to changes in the cloud field, and clouds changes occur on scales of seconds and less when viewed on scales of approximately 100m, the temporal resolution of cloud properties should be measured and characterized on second time scales. GCM time steps are typically on the order of an hour, but in order to obtain sufficient statistical representations of cloud properties in the parameterizations that are used as model inputs, averaged values of cloud properties should be calculated on time scales on the order of 10-100 s. The Holographic Airborne Rotating Lidar Instrument Experiment (HARLIE) provides exceptional temporal (100 ms) and spatial (30 m) resolution measurements of aerosol and cloud backscatter in three dimensions. HARLIE was used in a ground-based configuration in several recent field campaigns. Principal data products include aerosol backscatter profiles, boundary layer heights, entrainment zone thickness, cloud fraction as a function of altitude and horizontal wind vector profiles based on correlating the motions of clouds and aerosol structures across portions of the scan. Comparisons will be made between various cloud detecting instruments to develop a baseline performance metric.

Schwemmer, Geary K.↗
NASA NTRSDate not supplied

The Effect of Temperature on the Preservation of Volatile-Rich Lunar Samples

Introduction. The Moon’s south pole is a high-priority target for human exploration and scientific study. This interest is, in part, due to the presence of Permanently Shadowed Regions (PSRs), which could contain high concentrations of unique volatiles at cryogenic temperatures [1]. Returned samples from PSRs may include a unique combination of rocks, regolith, and volatile species, providing unprecedented insights into the history of the Solar System and the potential for resource utilization on the Moon. However, because PSR samples are cryogenic up-on collection, lunar polar sample return will eventually require cold stowage for the journey from the Moon to Earth. Without cold stowage, PSR sample return will likely result in phase changes and chemical reactions within the volatile component of the sample, which would negatively impact the resulting scientific studies of those samples. This abstract summarizes the initial results from an ongoing characterization of analog PSR samples at a range of temperatures, with the goal of defining the temperatures needed for a flight cold stowage freezer. Background. Based on remote sensing observations of the Moon [2], south polar PSRs range in temperature from ~120K for small and/or shallow PSRs to ~20K at the most extreme locations in large, deep PSRs. A range of volatiles have been hypothesized to exist at the surface or subsurface of the lunar poles [3-5 and others]. This hypothesis was verified when the LCROSS mission impacted the <50-K PSR in the crater Cabeus, detecting a range of volatiles from water to low condensation temperature species such as H2S and methane [6]. Species such as H2S and ammonia (also detected by LCROSS) are also highly reactive, and increase the likelihood of chemical reactions at elevated (non-cryogenic) temperatures. At the Johnson Space Center’s Planetary Exploration and Astromaterials Research Laboratory (JSC-PEARL), we have developed a volatile-bearing lunar simulant that incorporates several of the species detected by LCROSS [Table 1] mixed cryogenically with the USGS Lunar Highlands Type (LHT) regolith simulant. The new volatile-regolith simulant will be used to assess the degree of sample alteration at room temperature, -20°C, -80°C, and -196°C (liquid nitro-gen), over a two-week period. Room temperature samples represent those likely to be returned during initial missions without cold stowage, -20°C provides an analog to Apollo cold curated samples, -80°C is the temperature of multiple flight payload freezers (e.g., MELFI), and -196°C is analogous to lunar PSRs. Two weeks is an approximation of the time between sample collection and Earth return for initial Artemis missions. Over this period of time, sample head-space gases will be analyzed using a Universal Gas Analyzer (UGA, a type of mass spectrometer) coupled with a Baratron pressure sensor. After testing, the regolith component of the simulant will be purged of volatiles and preserved for future electron beam and/or FTIR analysis. Experimental Procedure. Volatile-regolith simulants will be produced as an initial homogenous batch; this batch will then be distributed into aliquots (gas chromatography/GC vials or cryo vials), ensuring that each sample has the same starting composition and conditions [Fig. 1]. In addition to the “full” simulant shown in Table 1, less complex simulant compositions will be used as baseline and control samples [Table 2]. Aliquots will be produced in triplicate for each simulant composition, storage temperature, and date of sampling. Headspace gases in all Day 0 samples will be analyzed by the UGA immediately. Cold storage samples for future analytical days will be placed in freezers appropriate to their target temperatures (-20°C, -80°C, -196°C). For ambient-temperature samples, regolith and regolith-water samples will be stored in a fume hood, while the full simulant will be stored in a sealed Parr vessel for safety; no other simulants (RWCM/ RWCM+) will be stored at ambient temperature for this test. Samples will be analyzed by UGA in this manner on each Analysis Day outlined in Table 2. Analytical Data. The UGA measures the partial pressures in a single sample aliquot over a set mass range of 0-105 atomic mass units (AMU) [Figure 2]; this set range was selected to slightly exceed the mass of the highest-mass expected reaction product (H2SO4). The Baratron complements the UGA by measuring the total pressure in the headspace of a sample vial. Coupled together, the quantitative abundances of gases will be monitored throughout the test. UGA analyses of the triplicate samples for each storage temperature, day, and simulant composition will be averaged, and standard deviations for each will be calculated. The compositions of starting species (shown in Table 1) will be characterized as a function of time, and the presence of any new compounds (reaction products) will be monitored as well. Total pressures will be recorded for each sample analysis, and any samples that show signs of leakage (e.g., a significant reduction in pressure or simulant volatiles) will be discarded. Anticipated Results. Testing is planned to begin in January 2022. The resulting data will allow compositional and phase changes in the volatile component of the simulants to be determined. Both the reduction in initial compounds and the addition of reaction products are expected to be observed. In addition, the relative efficacy of the different temperatures at pre-serving the initial composition of the simulants will be quantified. Finally, the regolith component of each sample will be argon-purged and stored in a controlled environment for future laboratory analysis. Compositional and morphological changes in the regolith are expected for samples above 0°C. This test will be repeated three times over the course of 2022. Understanding the effect of temperature on both the volatile and regolith components of analog lunar materials will allow requirements for a cold stowage freezer to be developed. The implementation of cold stowage for lunar polar missions will maximize the preservation of returned samples, enabling ground-breaking lunar and Solar System volatiles science for decades to come.

J L Mitchell↗
DOE OSTI2022

Design and Optimization of Structured Multi-Functional Trapping Catalysts for Conversion of Hydrocarbons and NOx from Diesel and Advanced Combustion Engines

Oxides of nitrogen in the form of nitric oxide (NO) and nitrogen dioxide (NO 2 ) commonly referred to as NOx, is one of the two chemical precursors that lead to ground-level ozone, a ubiquitous air pollutant in urban areas. A major source of NOx is generated by equipment and vehicles powered by diesel engines, which have a combustion exhaust that contains NOx in the presence of excess O 2 . Vehicular emission control catalysts are ineffective in eliminating CO, hydrocarbons, and NOx during engine cold-start when exhaust temperatures are below 200°C. The objective of the project was to develop and demonstrate a multi-functional, catalyzed trap that enables vehicles with advanced combustion strategies to meet Tier 3 emissions standards while achieving the 150 °C challenge for sustained co-oxidation of HCs and CO and ≥90% NO trapping and release during warmup. Specifically, the multi-functional Lean HC+NOx (LHCNT) was developed for application in the exhaust aftertreatment of conventional diesel engines and engines having low temperature combustion (LTC) regimes. Activities included the design and synthesis of adsorbents and catalysts, screening and evaluation. Passive NOx absorbers (PNA), hydrocarbon (HC) traps, and oxidation catalysts (OC) were evaluated for use in series or as integrated devices. Predictive tools were developed utilizing the characterization and analysis of these materials, and an emission system was designed and optimized utilizing the catalyst systems. Microkinetic models were developed for the PNA for the simple NO-only feed and complex feed containing CO, H 2 , and model hydrocarbons (ethylene and dodecane). A first-principles, mechanistic-based model of the PNA was developed which utilized molecular-scale estimates (density functional theory) of energy barriers, mechanistic-based kinetics and realistic treatments of the flow and transport processes. Two new oxidation catalysts were developed (PdCu alloy, mixed copper-ceria-cobalt oxide), both of which significantly lessened the detrimental inhibition by CO on hydrocarbon and NO oxidation. A method for lessening the detrimental impact of CO on PNA activity was developed that involves use of an oxidation catalyst upstream of the PNA. The SwRI Ectolab TM burner system was applied to evaluate the baseline PNA material and confirmed performance comparable to the benchflow PNA studies using simulated exhaust. Spatially-resolved mass spectrometry (SpaciMS) was used to measure the transient spatial profiles of reacting species spanning the length of a three-function LHCNT containing PNA, HCT, and OC. The findings from this study provide diesel vehicle and catalyst companies valuable information to develop more cost effective emission control catalysts which helps to expand the use of more fuel efficient diesel power. The fundamental modeling and experimental tools and findings from this project can be applied to catalyst technologies used in the energy and chemical industries. The project led to 14 publications in the peer-reviewed literature with 2 additional currently under review. Finally, the project also led to training of several doctoral students who were placed in research jobs in industry and academia. Specifically, Mugdha Ambast (UH) has joined Cummins, Kevin Gu (UVa) has joined GM, and Abhay Gupta (UH) is to join Caterpillar.

02 PETROLEUM↗
DOE OSTI2022

Multilevel Analysis, Design, and Modeling of Coupling Advanced Nuclear Reactors and Thermal Energy Storage in an Integrated Energy System

This report discusses the different options for coupling thermal energy storage (TES) systems to advanced nuclear power plants (A-NPPs) in order to enable flexible and hybrid plant operation. An advanced light-water reactor (A LWR), a high-temperature gas-cooled reactor (HTGR) and a liquid-metal fast reactor (LMFR) were selected as the initial use cases for demonstrating a thermally balanced energy storage coupling design for thermal power extraction. The models presented herein showcase several design considerations, focusing on optimal deployment methodologies for achieving steady-state and transient-state operation with minimum disruption to the nuclear power cycle. This first part of the study presents steady-state models developed using Aspen HYSYS®, with the thermal energy bypass for NPP-TES coupling being varied at up to 50%. The various components were sized using the Aspen Process Economic Analyzer (APEA) and Aspen Exchanger Design and Rating (EDR), when applicable. Cost functions from these models were developed using the latest publicly available data obtained from APEA V11. The TES-coupled A-NPP steady-state models and cost functions then provided a baseline for dynamic operation and process optimization by using Idaho National Laboratory (INL)’s Framework for Optimization of Resources and Economics (FORCE) tools. A stochastic optimization of the various energy storage systems coupled to the A-NPPs was then performed using the Risk Analysis Virtual Environment (RAVEN) and its dispatch optimization plugin, the Holistic Energy Resource Optimization Network (HERON). The signal processing and synthetic history capabilities of RAVEN were used to account for the unpredictable behavior of electricity markets. An autoregressive moving average (ARMA) model was used to analyze price signals from the Pennsylvania-New Jersey-Maryland (PJM) market and were applied to the HERON analysis in order to optimize a system with the best economics. Transient modeling evaluation was then performed using Modelica models within the HYBRID repository, which was developed at INL for the Department of Energy Integrated Energy Systems program for the characterization of dynamic integrated system behavior and feedback. This includes evaluation of the TES-coupled A-LWR systems’ impact on physical and thermal system response during imposed system demands. Additional TES-coupled reactor types, coupling approaches, markets, and TES technologies will be evaluated in future work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Design and Optimization of Structured Multi-Functional Trapping Catalysts for Conversion of Hydrocarbons and NOx from Diesel and Advanced Combustion Engines

Oxides of nitrogen in the form of nitric oxide (NO) and nitrogen dioxide (NO 2 ) commonly referred to as NOx, is one of the two chemical precursors that lead to ground-level ozone, a ubiquitous air pollutant in urban areas. A major source of NOx is generated by equipment and vehicles powered by diesel engines, which have a combustion exhaust that contains NOx in the presence of excess O 2 . Vehicular emission control catalysts are ineffective in eliminating CO, hydrocarbons, and NO x during engine cold-start when exhaust temperatures are below 200°C. The objective of the project was to develop and demonstrate a multi-functional, catalyzed trap that enables vehicles with advanced combustion strategies to meet Tier 3 emissions standards while achieving the 150 °C challenge for sustained co-oxidation of HCs and CO and ≥90% NO trapping and release during warmup. Specifically, the multi-functional Lean HC+NOx (LHCNT) was developed for application in the exhaust aftertreatment of conventional diesel engines and engines having low temperature combustion (LTC) regimes. Activities included the design and synthesis of adsorbents and catalysts, screening and evaluation. Passive NOx absorbers (PNA), hydrocarbon (HC) traps, and oxidation catalysts (OC) were evaluated for use in series or as integrated devices. Predictive tools were developed utilizing the characterization and analysis of these materials, and an emission system was designed and optimized utilizing the catalyst systems. Microkinetic models were developed for the PNA for the simple NO-only feed and complex feed containing CO, H 2 , and model hydrocarbons (ethylene and dodecane). A first-principles, mechanistic-based model of the PNA was developed which utilized molecular-scale estimates (density functional theory) of energy barriers, mechanistic-based kinetics and realistic treatments of the flow and transport processes. Two new oxidation catalysts were developed (PdCu alloy, mixed copper-ceria-cobalt oxide), both of which significantly lessened the detrimental inhibition by CO on hydrocarbon and NO oxidation. A method for lessening the detrimental impact of CO on PNA activity was developed that involves use of an oxidation catalyst upstream of the PNA. The SwRI Ectolab TM burner system was applied to evaluate the baseline PNA material and confirmed performance comparable to the benchflow PNA studies using simulated exhaust. Spatially-resolved mass spectrometry (SpaciMS) was used to measure the transient spatial profiles of reacting species spanning the length of a three-function LHCNT containing PNA, HCT, and OC. The findings from this study provide diesel vehicle and catalyst companies valuable information to develop more cost effective emission control catalysts which helps to expand the use of more fuel efficient diesel power. The fundamental modeling and experimental tools and findings from this project can be applied to catalyst technologies used in the energy and chemical industries. The project led to 14 publications in the peer-reviewed literature with 2 additional currently under review. Finally, the project also led to training of several doctoral students who were placed in research jobs in industry and academia. Specifically, Mugdha Ambast (UH) has joined Cummins, Kevin Gu (UVa) has joined GM, and Abhay Gupta (UH) is to join Caterpillar.

42 ENGINEERING↗
DOE OSTI2022

A System for Standardizing and Combining U.S. Environmental Protection Agency Emissions and Waste Inventory Data

The U.S. Environmental Protection Agency (USEPA) provides databases that agglomerate data provided by companies or states reporting emissions, releases, wastes generated, and other activities to meet statutory requirements. These databases, often referred to as inventories, can be used for a wide variety of environmental reporting and modeling purposes to characterize conditions in the United States. Yet, users are often challenged to find, retrieve, and interpret these data due to the unique schemes employed for data management, which could result in erroneous estimations or double-counting of emissions. To address these challenges, a system called Standardized Emission and Waste Inventories (StEWI) has been created. The system consists of four python modules that provide rapid access to USEPA inventory data in standard formats and permit filtering and combination of these inventory data. When accessed through StEWI, reported emissions of carbon dioxide to air and ammonia to water are reduced approximately two- and four-fold, respectively, to avoid duplicate reporting. StEWI will greatly facilitate the use of USEPA inventory data in chemical release and exposure modeling and life cycle assessment tools, among other things. To date, StEWI has been used to build the recent USEEIO model and the baseline electricity life cycle inventory database for the Federal LCA Commons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess

The nature of the Fermi γ-ray Galactic Center Excess (GCE) has remained a persistent mystery for over a decade. Although the excess is broadly compatible with emission expected due to dark matter annihilation, an explanation in terms of a population of unresolved astrophysical point sources e.g., millisecond pulsars, remains viable. The effort to uncover the origin of the GCE is hampered in particular by an incomplete understanding of diffuse emission of Galactic origin. This can lead to spurious features that make it difficult to robustly differentiate smooth emission, as expected for a dark matter origin, from more “clumpy” emission expected from a population of relatively bright, unresolved point sources. Here, we use recent advancements in the field of simulation-based inference, in particular density estimation techniques using normalizing flows, in order to characterize the contribution of modeled components, including unresolved point source populations, to the GCE. Compared to traditional techniques based on the statistical distribution of photon counts, our machine learning-based method is able to utilize more of the information contained in a given model of the Galactic Center emission, and in particular can perform posterior parameter estimation while accounting for pixel-to-pixel spatial correlations in the γ-ray map. This makes the method demonstrably more resilient to certain forms of model misspecification. On application to Fermi data, the method generically attributes a smaller fraction of the GCE flux to unresolved point sources when compared to traditional approaches. We nevertheless infer such a contribution to make up a non-negligible fraction of the GCE across all analysis variations considered, with at least $38^{+9}_{-19}$% of the excess attributed to unresolved point sources in our baseline analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2004

Mechanisms of postspaceflight orthostatic hypotension: low alpha1-adrenergic receptor responses before flight and central autonomic dysregulation postflight

Although all astronauts experience symptoms of orthostatic intolerance after short-duration spaceflight, only approximately 20% actually experience presyncope during upright posture on landing day. The presyncopal group is characterized by low vascular resistance before and after flight and low norepinephrine release during orthostatic stress on landing day. Our purpose was to determine the mechanisms of the differences between presyncopal and nonpresyncopal groups. We studied 23 astronauts 10 days before launch, on landing day, and 3 days after landing. We measured pressor responses to phenylephrine injections; norepinephrine release with tyramine injections; plasma volumes; resting plasma levels of chromogranin A (a marker of sympathetic nerve terminal release), endothelin, dihydroxyphenylglycol (DHPG, an intracellular metabolite of norepinephrine); and lymphocyte beta(2)-adrenergic receptors. We then measured hemodynamic and neurohumoral responses to upright tilt. Astronauts were separated into two groups according to their ability to complete 10 min of upright tilt on landing day. Compared with astronauts who were not presyncopal on landing day, presyncopal astronauts had 1). significantly smaller pressor responses to phenylephrine both before and after flight; 2). significantly smaller baseline norepinephrine, but significantly greater DHPG levels, on landing day; 3). significantly greater norepinephrine release with tyramine on landing day; and 4). significantly smaller norepinephrine release, but significantly greater epinephrine and arginine vasopressin release, with upright tilt on landing day. These data suggest that the etiology of orthostatic hypotension and presyncope after spaceflight includes low alpha(1)-adrenergic receptor responsiveness before flight and a remodeling of the central nervous system during spaceflight such that sympathetic responses to baroreceptor input become impaired.

Clinical Trial↗