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At least 1,243 records · Page 69

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NASA NTRSDate not supplied

Atmospheric Composition Forecast Model Evaluation Using Ozone Measurements Collected by the Langley Mobile Ozone Lidar

The Langley Mobile Ozone Lidar (LMOL) is a mobile ground based lidar system based at NASA Langley in Hampton, Virginia. Between 2022 and 2024, LMOL collected over 2500 hours of ozone measurements for a range of different atmospheric conditions, including calm days, stratospheric intrusions, surface frontal passages, and long-range transported wildfire smoke plumes. Here, the data is used to evaluate the forecast accuracy of NASA’s Global GEOS Composition Forecasting (GEOS-CF) model. GEOS-CF makes daily three-dimensional forecasts of trace gases and aerosol species. Overall, for calm periods, the forecast model predicts lower tropospheric ozone at NASA Langley with reasonable accuracy (within 20%). The model best predicts the timing and extent of stratospheric intrusions but often vary in the magnitude of the ozone mixing ratio. Among the other types of atmospheric conditions, there is more variability in the model forecasts. Based on this analysis, model forecasts are utilized to determine future data acquisition opportunities with the goal of providing feedback to the modeling teams, thereby enabling them to better understand the model biases and improve the model forecasts of ozone during these different atmospheric conditions.

Daniel B Phoenix↗

DOE OSTI2025

How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive

This dataset contains model output and input data, as well as source code examples for the Terrestrial Ecosystem Model with the Dynamic Vegetation Model and Dynamic Organic Soil (DVM-DOS-TEM) for the field sites Imnavait creek and the Bonanza creek Long Term Ecological Research Network (LTER). The data covers simulations from the last glacial maximum (LGM) until 2100 for a selection of paleo scenarios, setting the mean temperature of the LGM up to 10°C lower than pre-industrial conditions. The model structure was modulated to represent various model versions, and this dataset contains the relevant changes in the source code. The raw output data, the processed statistical data, the setup and processing scripts as well as parameter value distribution files from a parameter sensitivity analysis are included as well. Model outputs include active layer depth, organic soil carbon, soil layer depths, gross primary productivity (GPP) with and without nitrogen limitation, net primary productivity (NPP), soil liquid water content, heterotrophic, maintenance, and growth respiration, soil temperature, and vegetation carbon (*.nc files). The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research.Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Flame Chemistry Workshop: a perspective on challenges and strategic actions in combustion experiments and chemical kinetics modeling

Continued progress in the development of predictive models for combustion chemistry—including ignition and flame behavior, species evolution, and combustion system performance—relies on overcoming enduring and emerging challenges in experimental measurements, theoretical formulations, and chemical kinetics mechanism construction. As combustion science continues to coincide with advances in sustainable fuels development, plasma technologies, and automated modeling capabilities, the need for coordinated, community-driven strategies is essential. The Flame Chemistry Workshop (FCWS), held biennially before the International Symposium on Combustion, serves as a dedicated platform to identify, consolidate, and address these challenges in a structured and collaborative manner. This perspective arises from discussions at the 7th FCWS in Milan, Italy (2024), and presents a collective view of the critical barriers currently limiting progress. Across the five technical domains discussed during the 7th FCWS – sustainable fuels combustion, advanced diagnostics for combustion measurements, experiments and modeling in plasma combustion, artificial intelligence and automated methods for theory and mechanisms generation, and chemical kinetic models—a series of persistent and emerging scientific challenges were identified, highlighting the need for deeper integration between three areas: theory, experiments, and modeling. In conclusion, the present article concisely describes present challenges that were identified in each of the technical domains in an effort to streamline and coordinate solutions to accelerate progress in combustion science.

Chemical kinetics↗

NASA NTRS1979

Advanced development of atmospheric models

A set of atmospheric analysis and prediction models was developed in support of the SEASAT Program existing objective analysis models which utilize a 125x125 polar stereographic grid of the Northern Hemisphere, which were modified in order to incorporate and assess the impact of (real or simulated) satellite data in the analysis of a two-day meteorological scenario in January 1979. Program/procedural changes included: (1) a provision to utilize winds in the sea level pressure and multi-level height analyses (1000-100 MBS); (2) The capability to perform a pre-analysis at two control levels (1000 MBS and 250 MBS); (3) a greater degree of wind- and mass-field coupling, especially at these controls levels; (4) an improved facility to bogus the analyses based on results of the preanalysis; and (5) a provision to utilize (SIRS) satellite thickness values and cloud motion vectors in the multi-level height analysis.

Kesel, P. G.↗

NASA NTRS2006

Cryogenic Tank Modeling for the Saturn AS-203 Experiment

A computational fluid dynamics (CFD) model is developed for the Saturn S-IVB liquid hydrogen (LH2) tank to simulate the 1966 AS-203 flight experiment. This significant experiment is the only known, adequately-instrumented, low-gravity, cryogenic self pressurization test that is well suited for CFD model validation. A 4000-cell, axisymmetric model predicts motion of the LH2 surface including boil-off and thermal stratification in the liquid and gas phases. The model is based on a modified version of the commercially available FLOW3D software. During the experiment, heat enters the LH2 tank through the tank forward dome, side wall, aft dome, and common bulkhead. In both model and test the liquid and gases thermally stratify in the low-gravity natural convection environment. LH2 boils at the free surface which in turn increases the pressure within the tank during the 5360 second experiment. The Saturn S-IVB tank model is shown to accurately simulate the self pressurization and thermal stratification in the 1966 AS-203 test. The average predicted pressurization rate is within 4% of the pressure rise rate suggested by test data. Ullage temperature results are also in good agreement with the test where the model predicts an ullage temperature rise rate within 6% of the measured data. The model is based on first principles only and includes no adjustments to bring the predictions closer to the test data. Although quantitative model validation is achieved or one specific case, a significant step is taken towards demonstrating general use of CFD for low-gravity cryogenic fluid modeling.

Grayson, Gary D.↗

NASA NTRS1992

Geometry and mass model of ionizing radiation experiments on the LDEF satellite

Extensive measurements related to ionizing radiation environments and effects were made on the LDEF satellite during its mission lifetime of almost 6 years. These data, together with the opportunity they provide for evaluating predictive models and analysis methods, should allow more accurate assessments of the space radiation environment and related effects for future missions in low Earth orbit. The LDEF radiation dosimetry data is influenced to varying degrees by material shielding effects due to the dosimeter itself, nearby components and experiments, and the spacecraft structure. A geometry and mass model is generated of LDEF, incorporating sufficient detail that it can be applied in determining the influence of material shielding on ionizing radiation measurements and predictions. This model can be used as an aid in data interpretation by unfolding shielding effects from the LDEF radiation dosimeter responses. Use of the LDEF geometry/mass model, in conjunction with predictions and comparisons with LDEF dosimetry data currently underway, will also allow more definitive evaluations of current radiation models for future mission applications.

Colborn, B. L.↗

DOE OSTI2025

Characterization of Arsenic and Selenium in Coal Fly Ash to Improve Evaluations for Disposal and Reuse Potential (Final Technical Report)

Coal fly ash is a high volume waste material that is discarded in landfills and surface water impoundments across the U.S. and is also widely recycled for a variety of applications. The leaching of potential of contaminants of concern, such as arsenic (As) and selenium (Se), is often the driver of risk assessments for coal ash disposal and reuse. The extent of leachable As and Se depends on several factors related to environmental conditions and fly ash characteristics. Previous studies employed various methods to delineate the concentration, chemical form, and distribution of As and Se in fly ash materials. However, few studies have attempted to directly correlate these properties to mobilization parameters relevant to disposal and reuse. Instead, the coal residuals industries often rely upon standardized leaching protocols that can be laborious or involve hazardous chemicals. The goals of the project were to: 1) Develop and evaluate a characterization protocol that can be used to screen fly ash samples for leachability of As and Se; 2) Characterize As, Se, and associated constituents of fly ash particles at multiple length scales (nanometer to micrometer) to determine if elemental associations differ as a function of the resolution of characterization; and 3) Establish a predictive model for the chemical composition of coal ash produced annually at major U.S. coal fired power facilities on 50-year national coal supply records. For the first objective, we performed leaching experiments with 52 fly ash samples collected from 15 different U.S. power plants and representing coal feedstocks from the three major domestic coal regions. For this work, we assessed the mobilization potential of As and Se in fly ash based on standardized leaching protocols and performed multivariate and lasso regression analyses to explore correlations of leachable As and Se contents with characteristics such as major element contents, loss on ignition (LOI) and pH. The results of regression models indicated that major elements (Fe, Ca, Al) for a wide range of fly ashes can serve as predictor variables for the leaching potential of As, but not for Se. LOI and pH were not important predictive variables in the models. Both regression approaches resulted in relatively strong fits for leachable As (correlation coefficient R 2 = 0.78 for both models) compared to models for leachable Se (R 2 = 0.49). Overall, these results suggest that correlation models combined with on-site elemental analysis with portable analyzers may enable a screening method for leachable As in coal ash. For the second objective, we utilized nanoscale 2-D imaging (30-50 nm spot size) with the Hard X-ray Nanoprobe (HXN) in combination with microprobe X-ray capabilities (~5 µm resolution) to determine As and Se elemental associations in fly ash particles. Speciation of As and Se was also measured at the nano- to microscale with X-ray absorption spectroscopy. The enhanced resolution of HXN showed As and Se that were diffusely located around or comingled with Ca- and Fe-rich particles. The results also showed nanoparticles of Se attached to the surface of fly ash grains. Overall, a comparison of As and Se species across scales highlights the heterogeneity and complexity of chemical associations for these trace elements of concern in coal fly ash. For the final objective, we developed a predictive model for major element composition of coal ash in reserve at disposal sites of major U.S. coal fired power plants. This model was constructed from coal purchase records of 705 power stations from 1973-2022 and was trained on coal ash composition data showing that coal ash elemental composition is strongly associated with the source of feedstock coal. The model showed regional shifts in the major element contents of ash produced by power plants in the last 50 years, particularly for calcium and iron (expressed as %CaO and %Fe 2 O 3 ), as coal-fired power stations changed their source of coal over this time frame. Our approach enables an estimation of coal ash chemical composition that is stored in waste impoundments at individual power stations. Such information can help delineate the regional market potential for material applications that would utilize coal ash harvested from disposal sites across the U.S.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning

Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred as hallucinations. Retrieval-augmented LLMs provide a non-parametric approach to solve these issues by retrieving relevant information from external data sources and augment the training process. These models helps to trace evidence from an externally provided knowledge base allowing the model predictions to be better interpreted and verified. In this work, we critically evaluate these models in their ability to perform in scientific document reasoning tasks. To this end, we tuned multiple such model variants with science-focused instructions and evaluated them on a scientific document reasoning benchmark for the usefulness of the retrieved document passages. Our findings suggest that models justify predictions in science tasks with fabricated evidence and leveraging scientific corpus as pretraining data does not alleviate the risk of evidence fabrication.

• Artificial intelligence (AI) / machine learning ↗

DOE OSTI2025

Microbial vitamin biosynthesis links gut microbiota dynamics to chemotherapy toxicity

ABSTRACT Dose-limiting toxicities pose a major barrier to cancer treatment. While preclinical studies show that the gut microbiota influences and is influenced by anticancer drugs, data from patients paired with careful side effect monitoring remains limited. Here, we investigate capecitabine (CAP)-microbiome interactions through longitudinal metagenomic sequencing of stool from 56 advanced colorectal cancer patients. CAP significantly altered the gut microbiome, enriching for menaquinol (vitamin K2) biosynthesis genes. Transposon library screens, targeted gene deletions, and media supplementation revealed that menaquinol biosynthesis protectsEscherichia colifrom drug toxicity. Stool menaquinol gene and metabolite levels were associated with decreased peripheral sensory neuropathy. Machine learning models trained in this cohort predicted toxicities in an independent cohort. Taken together, these results suggest treatment-associated increases in microbial vitamin biosynthesis serve a chemoprotective role for bacterial and host cells. Further, our findings provide a foundation for in-depth mechanistic dissection, human intervention studies, and extension to other cancer treatments. IMPORTANCE Side effects are common during the treatment of cancer. The trillions of microbes found within the human gut are sensitive to anticancer drugs, but the effects of treatment-induced shifts in gut microbes for side effects remain poorly understood. We profiled gut microbes in colorectal cancer patients treated with capecitabine and carefully monitored side effects. We observed a marked expansion in genes for producing vitamin K2 (menaquinone). Vitamin K2 rescued gut bacterial growth and was associated with decreased side effects in patients. We then used information about gut microbes to develop a predictive model of drug toxicity that was validated in an independent cohort. These results suggest that treatment-associated increases in bacterial vitamin production protect both bacteria and host cells from drug toxicity, providing new opportunities for intervention and motivating the need to better understand how dietary intake and bacterial production of micronutrients like vitamin K2 influence cancer treatment outcomes.

Microbiology↗

NASA NTRS1984

An analysis of return flux from the Space Shuttle Orbiter RCS engines

The return flux from the Space Shuttle Orbiter reaction control system (RCS) engines to sensors in the open payload bay has been analyzed on the basis of Shuttle/Payload Contamination Evaluation (SPACE II) model predictions and orbital flight measurements. Model data are presented showing the variations of molecular return flux values with Orbiter orientation and instrument direction. The effects of multiple molecular collisions within RCS engine plumes and in their vicinity are discussed. These collisions significantly influence the amount of plume molecules returning to payload instruments and, therefore, the amount of contaminants received.

Ehlers, H. K. F.↗

NASA NTRS1985

Solar cycle variations of the anomalous cosmic ray component

The intensity of the anomalous cosmic ray component, consisting of He, N, O, and Ne, has long been known to be especially sensitive to the effects of solar modulation. Following its discovery in 1972, this component dominated the quiet-time flux of cosmic ray nuclei below approximately 30 MeV/nucleon during the 1972 to 1978 solar minimum, but then became essentially unobservable at 1 AU with the approach of solar maximum conditions. One recent theoretical model predicts substantial differences in the intensity of the anomalous fluxes from one solar minimum period to the next because of the reversal of the solar magnetic field. Using data from the Caltech experiments on IMP-8 and ICE (ISEE-3), the intensity of anomalous O and He at 1 AU during the years 1972 to 1985 is reported. We hope to determine whether the anomalous fluxes will return to their 1972 to 1978 levels, as predicted by spherically symmetric modulation models, or whether they will fail to return to 1 AU, as suggested by modulation models in which gradient and curvature drifts dominate.

Mewaldt, R. A.↗

NASA NTRS1985

Solar cycle variations of the anomalous cosmic ray component

The intensity of the anomalous cosmic ray component, consisting of He, N, O, and Ne, has long been known to be especially sensitive to the effects of solar modulation. Following its discovery in 1972, this component dominated the quiet time flux of cosmic ray nuclei below approx. 30 MeV/nucleon during the 1972 to 1978 solar minimum, but then became essentially unobservable at 1 AU with the approach of solar maximum conditions. One recent theoretical model predicts substantial differences in the intensity of the anomalous fluxes from one solar minimum period to the next because of the reversal of the solar magnetic field. Using data from the Caltech experiments on IMP-8 and ICE (ISEE-3), the intensity of anomalous O and He at 1 AU during the years 1972 to 1985 is reported in particular. Whether the anomalous fluxes will return to their 1972-1978 levels, as predicted by spherically symmetric modulation models, or whether they will fail to return to 1 AU, as suggested by modulation models in which gradient and curvature drifts dominate are to be determined. The preliminary analysis of data from 1984 shows that the intensity of 8 to 27 MeV/nucleon O is still more than an order of magnitude below its 1972 to 1978 levels, while the intensity of 25 to 43 MeV/nucleon He is a factor of Approx. 8 below its maximum level in 1977.

Mewaldt, R. A.↗

NASA NTRS1985

Mars atmospheric circulation - Aspects from Viking Landers

Winds measured by the two Viking Landers have been filtered and then compared with predictions from the general circulation model and to Orbiter observations of clouds and surface phenomena that indicate wind direction. This was done to determine the degree to which filtered winds may represent aspects of the general circulation. Excellent agreement was found between wind direction data from Lander 1 and the model predictions and Orbiter observations. For Lander 2, agreement was generally good, but there were periods of disagreement which indicate that the filtering did not remove other extraneous effects. It is concluded that Lander 1 gives a good representation of the general circulation at 22.5 deg N latitude but that Lander 2 is suspect. Most wind data from Lander 1 have yet to be analyzed. It appears that when analyzed these Lander 1 data (covering 3.5 Mars years) can provide information about interannual variations in the general circulation at the Lander latitude.

Ryan, J. A.↗

NASA NTRS1989

Observations and numerical simulations of precipitation development is seeded clouds over the Sierra Nevada

The evolution of precipitation in seeded wintertime orographically induced convective and stratiform clouds with embedded convection were studied using in situ observations and particle growth and trajectory models. The particle growth model of Heymsfield embedded in a kinematic flow field representative of the Sierra barrier was used to study the ice particle growth by diffusion, accretion and subsequent fall trajectories. The particles observed by the aircraft were classified into habits. The growth of observed particles were compared with the model predicted evolution. Using the aggregation model of Heymsfield, the observation of formation of aggregates is less than 10 minutes was verified. The key findings of this study are presented.

Prasad, N.↗

NASA NTRS1991

Modeling of chemical vapor infiltration for ceramic composites reinforced with layered, woven fabrics

A homogeneous model is developed for the chemical vapor infiltration by one-dimensional diffusion into a system of layered plies consisting of woven tows containing bundles of filaments. The model predictions of the amount of deposition and the porosity of the sample as a function of time are compared with the predictions of a recent nonhomogeneous model with aligned holes formed by the weave. The nonhomogeneous model allows for diffusion through the aligned holes, into the spaces between plies, and into the gaps around filaments; i.e., three diffusion equations apply. Relative to the nonhomogeneous results, the homogeneous model underestimates the amount of deposition, since the absence of holes and spaces allows earlier occlusion of gaps around filaments and restricts the vapor infiltration.

Chung, Gui-Yung↗

NASA NTRS1999

Canopy Cover Predictions using Ground Observations and Remotely Sensed Data

Maps of vegetation status are needed at many scales, from the field level to monitor ecosystem condition to the global level to understand the carbon cycle. Status is quantified by such variables as leaf area index, biomass, and fraction of canopy cover. Current methods of predicting vegetation variables use remote sensing data to provide a spatially exhaustive data source. In a study in western Montana, several hundred ground observations made by the US Forest Service on tenth-acre conifer plots were used to develop aspatial regression and geostatistical prediction models. Normalized Difference Vegetation Index (NDVI) values from Landsat Thematic Mapper images were used as ancillary data. These models were then used to predict canopy cover at unsampled locations in a 97 square kilometer region on the boundary of the Flathead National Forest and the Bob Marshall Wilderness. Independent data from two dates six years apart were used for validation. Given the assumption that actual canopy cover remained relatively unchanged within this time period, partial validation can be achieved by measuring the correspondence of the two maps. This criterion results in ranking the aspatial regression maps as less accurate than the geostatistically generated maps. The geostatistical approach emphasizes ground measurements more heavily than does aspatid regression. Geostatistical simulations of canopy cover also provide a means of describing uncertainty about the patterns of canopy cover.

Dungan, Jennifer L.↗

NASA NTRS2007

Alaska at the Crossroads of Migration: Space Based Ornithology

Understanding bird migration on a global scale is one of the most compelling and challenging problems of modern biology with major implications for human health and conservation biology. Revolutionary advances in remote sensing now provide us with near real-time measurements of atmospheric and land surface conditions at high spatial resolution over entire continents. We use spatially-explicit, individual based bird migration models driven by numerical weather prediction models of atmospheric conditions, dynamic habitat suitability maps derived from remotely sensed land surface conditions, biophysiological models, and biological field data to simulate migration routes, timing, energy budgets, and survival of individual birds and populations. Long-distance migratory birds travel annually between breeding grounds in Alaska and wintering grounds in Latin Amierica. Approximately 25% of these species are potential vectors of Avian Influenza. Alaska is at the crossroads of Asian and New World migratory flyways and is likely to be a point of introduction of Asian H5N1 AI into the western hemisphere. If/when an infected bird is detected, a pressing question will be where was this bird several days ago, and where is it likely to go after it was released from the survey site? Answers to such questions will increase effectiveness of AI surveillance and mitigation measures. From a conservation perspective, Alaska's diverse landscape provides breeding sites for many migrants, and climatic and land surface changes along migratory flyways in the western hemisphere may reduce bird survival and physical condition upon arrival at Alaskan breeding territories, success and migrant populations.

Deppe, Jill↗

NASA NTRS2003

Material Damping Experiments at Cryogenic Temperatures

A unique experimental facility has been designed to measure damping of materials at cryogenic temperatures. The test facility pays special attention to removing other sources of damping in the measurement by avoiding frictional interfaces, decoupling the test specimen from the support system, and by using a non-contacting measurement device; Damping data is obtained for materials (AI, GrEp, Be, Fused Quartz), strain amplitudes (less than 10-6 ppm), frequencies (20Hz-330Hz) and temperatures (20K-293K) relevant to future precision optical space missions. The test data shows a significant decrease in viscous damping at cryogenic temperatures and can be as low as 10-4%, but the amount of the damping decrease is a function of frequency and material. Contrary to the other materials whose damping monotonically decreased with temperature, damping of Fused Quartz increased substantially at cryo, after reaching a minimum at around l50 K. The damping is also shown to be insensitive to strain for low strain levels. At room temperatures, the test data correlates well to the analytical predictions of the Zener damping model. Discrepancies at cryogenic temperatures between the model predictions and the test data are observed.

cryogenic↗