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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Ocean Carbon From Space: Current Status and Priorities for the Next Decade

The ocean plays a central role in modulating the Earth’s carbon cycle. Monitoring how the ocean carbon cycle is changing is fundamental to managing climate change. Satellite remote sensing is currently our best tool for viewing the ocean surface globally and systematically, at high spatial and temporal resolutions, and the past few decades have seen an exponential growth in studies utilising satellite data for ocean carbon research. Satellite-based observations must be combined with in-situ observations and models, to obtain a comprehensive view of ocean carbon pools and fluxes. To help prioritise future research in this area, a workshop was organised that assembled leading experts working on the topic, from around the world, including remote-sensing scientists, field scientists and modellers, with the goal to articulate a collective view of the current status of ocean carbon research, identify gaps in knowledge, and formulate a scientific roadmap for the next decade, with an emphasis on evaluating where satellite remote sensing may contribute. A total of 449 scientists and stakeholders participated (with balanced gender representation), from North and South America, Europe, Asia, Africa, and Oceania. Sessions targeted both inorganic and organic pools of carbon in the ocean, in both dissolved and particulate form, as well as major fluxes of carbon between reservoirs (e.g., primary production) and at interfaces (e.g., air-sea and land–ocean). Extreme events, blue carbon and carbon budgeting were also key topics discussed. Emerging priorities identified include: expanding the networks and quality of in-situ observations; improved satellite retrievals; improved uncertainty quantification; improved understanding of vertical distributions; integration with models; improved techniques to bridge spatial and temporal scales of the different data sources; and improved fundamental understanding of the ocean carbon cycle, and of the interactions among pools of carbon and light. We also report on priorities for the specific pools and fluxes studied, and highlight issues and concerns that arose during discussions, such as the need to consider the environmental impact of satellites or space activities; the role satellites can play in monitoring ocean carbon dioxide removal approaches; economic valuation of the satellite based information; to consider how satellites can contribute to monitoring cycles of other important climatically-relevant compounds and elements; to promote diversity and inclusivity in ocean carbon research; to bring together communities working on different aspects of planetary carbon; maximising use of international bodies; to follow an open science approach; to explore new and innovative ways to remotely monitor ocean carbon; and to harness quantum computing. Overall, this paper provides a comprehensive scientific roadmap for the next decade on how satellite remote sensing could help monitor the ocean carbon cycle, and its links to the other domains, such as terrestrial and atmosphere.

oceanography↗
NASA NTRS2021

Variabilities in Pm2.5 and Black Carbon Surface Concentrations Reproduced by Aerosol Optical Properties Estimated by In-Situ Data, Ground Based Remote Sensing and Modeling

Because of the increased temporal and spatial resolutions of the sensors onboard recently launched satellites, satellite-based surface aerosol concentration, which is usually estimated from the aerosol optical depth (AOD), is expected to become a strategic tool for air quality studies in the future. By further exploring the relationships of aerosol concentrations and their optical properties using ground observations, the accuracies of these products can be improved. Here, we analyzed collocated observations of surface mass concentrations of fine particulate matter (PM2.5) and black carbon (BC), as well as columnar aerosol optical properties from a sky radiometer and aerosol extinction profiles obtained by multi-axis differential optical absorption spectroscopy (MAX-DOAS), during the 2019–2020 period. We focused the analyses on a daily scale, emphasizing the role of the ultraviolet (UV) spectral region. Generally, the correlation between the AOD of the fine fraction (i.e., fAOD) and the PM2.5 surface concentration was moderately strong, regardless of considerations of boundary layer humidity and altitude. In contrast, the fAOD of the partial column below 1 km, which was obtained by combining sky radiometer and MAX-DOAS retrievals, better reproduced the variability of the PM2.5 and resulted in a linear relationship. In the same manner, we demonstrated that the absorption AOD of the fine fraction (fAAOD) of the partial column was related to the variability of the BC concentration. Analogous analyses based on aerosol products from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) confirmed these findings and highlighted the importance of the shape of the aerosol profile. Overall, our results indicated a remarkable consistency among the retrieved datasets, and between the datasets and MERRA-2 products. These results confirmed the well-known sensitivity to aerosol absorption in the UV spectral region; they also highlighted the efficacy of combined MAX-DOAS and sky radiometer observations.

Alessandro Damiani↗
DOE OSTI2024

Towards in-situ certification of additively manufactured parts: the vital roles of physics-based and data-driven models

Certifying additively manufactured (AM) parts in-situ at the completion of a build is an enticing prospect, as it can help reduce the high costs associated with post-build testing and evaluation. However, achieving this goal presents significant challenges that may keep it aspirational for the foreseeable future. Nonetheless, incremental progress can pave the way forward. A critical aspect of in-situ certification involves continuous quality checking due to the random nature of the AM process and the difficulties in detecting defects or anomalies once layers are built over. While real-time in-situ monitoring strategies assisted by machine learning (ML) play a pivotal role in auditing part quality, they must ideally be supported by real-time (or near real-time) adaptive process control enabled by ML-assisted decision-making. By analyzing in-situ monitoring data in real-time (or near real- time) to dynamically adjust manufacturing parameters, such intervention can ensure AM parts are built to meet stringent certification standards. This can be achieved virtually by using high-fidelity performance models for the physical testing and evaluation tasks. In this short editorial, we discuss the key contributions made by data-driven and physics-based models in providing intelligence to the monitoring and process control tasks underpinning in-situ certification and in the simulation of the build’s performance under test and service conditions. While our focus lies in metal AM, the concepts discussed here are also relevant to other AM processes.

: In-situ monitoring↗
DOE OSTI2025

AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition

Abstract. Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.

Mahowald, Natalie M. (ORCID:000000022873997X)↗
DOE OSTI2023

Resolving dynamic mineral-organic interactions in the rhizosphere by combining in-situ microsensors with plant-soil reactive transport modeling

Associations between minerals and organic matter represent one of the most important carbon storage mechanisms in soils. Plant roots are major sources of soil carbon, and resolving the dynamics and dominance of microbial consumption versus mineral sorption of root-derived carbon is critical to understanding soil carbon storage. Here we integrate in-situ rhizosphere microsensor and plant physiological measurements with a 3-D plant-soil reactive transport model to explore the fate of dissolved organic carbon (DOC) in the rhizosphere, particularly its microbial consumption and interaction with Fe oxide minerals. Over several days, a microdialysis probe sampling pore water at the root-soil interface of growing Vicia faba roots in live soil, revealed clear diel patterns of DOC concentration. Daytime DOC spikes coincided with peaks in leaf-level photosynthesis rates and were accompanied by declining redox potential and dissolved oxygen as well as increasing pH in the rhizosphere. Incorporating microsensor data into our modeling framework showed that the measured rapid loss of DOC after each mid-day spike could not be explained by consumption via aerobic respiration, nor via anaerobic respiration dominated by Fe oxide reduction. Rather, in the model, a large fraction of rhizosphere DOC was rapidly immobilized each day by adsorption to Fe oxides. Further, modeled microbial Fe reduction (fueled by DOC) did not mobilize significant organic carbon from Fe oxides during the day. Instead, the model predicted equilibrium desorption of organic carbon from Fe oxides at night. This new mechanistic modeling framework, which couples aboveground plant physiological measurements with non-destructive high-resolution monitoring of rhizosphere processes, has great potential for exploring the dynamics and balance of the various microbial reactions and mineral interactions controlling carbon transformations and storage in soils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2014

Evaluation of Mixed-Phase Microphysics Within Winter Storms Using Field Data and In Situ Observations

Snow prediction within models is sensitive to the snow densities, habits, and degree of riming within the BMPs. Improving these BMPs is a crucial step toward improving both weather forecasting and climate predictions. Several microphysical schemes in the Weather Research and Forecasting (WRF) model down to 1.33-km grid spacing are evaluated using aircraft, radar, and ground in situ data from the Global Precipitation Mission Cold-season Precipitation Experiment (GCPEx) experiment over southern Ontario, as well as a few years (12 winter storms) of surface measurements of riming, crystal habit, snow density, and radar measurements at Stony Brook, NY (SBNY on north shore of Long Island) during the 2009-2012 winter seasons. Surface microphysical measurements at SBNY were taken every 15 to 30 minutes using a stereo microscope and camera, and snow depth and snow density were also recorded. During these storms, a vertically-pointing Ku band radar was used to observe the vertical evolution of reflectivity and Doppler vertical velocities. The GCPex presentation will focus on verification using aircraft spirals through warm frontal snow band event on 18 February 2012. All the BMPs realistically simulated the structure of the band and the vertical distribution of snow/ice aloft, except the SBU-YLIN overpredicted slightly and Thompson (THOM) underpredicted somewhat. The Morrison (MORR) scheme produced the best slope size distribution for snow, while the Stony Brook (SBU) underpredicted and the THOM slightly overpredicted. Those schemes that have the slope intercept a function of temperature (SBU and WSM6) tended to perform better for that parameter than others, especially the fixed intercept in Goddard. Overall, the spread among BMPs was smaller than in other studies, likely because there was limited riming with the band. For the 15 cases at SBNY, which include moderate and heavy riming events, the non-spherical snow assumption (THOM and SBU-YLIN) simulated a more realistic distribution of reflectivity than spherical snow assumptions in the WSM6 and MORR schemes. The MORR, WSM6, and SBU schemes are comparable to the observed velocity distribution in light and moderate riming periods. The THOM is approx. 0.25 m/s too slow with its velocity distribution in these periods. In heavier riming, the vertical Doppler velocities in the WSM6, THOM, and MORR schemes were approx. 0.25 m/s too slow, while the SBU was 0.25 to 0.5 m/s too fast because of some excessive cloud water issues.

Colle, Brian A.↗
DOE OSTI2023

In Situ Raman Mapping of Si Island Electrodes and Stress Modeling as a Function of Lithiation and Size

Si is known for cracking and delamination during electrochemical cycling of a battery due to the large volume change associated with Li insertion and extraction. However, it has been found experimentally that patterned Si island electrodes that are 200 nm thick and less than 7 μm wide can deform in a purely elastic manner. Inspired by this, we performed in situ Raman stress characterization of model poly-crystalline Si island electrodes using an electrochemical cell coupled with an immersion objective lens and designed for a short working distance. A 5 μm wide Si island electrode showed a parabolic stress profile during lithiation, while for a 15 μm Si island electrode, a stress plateau in the center of the electrode was observed. A continuum model with coupled electro-chemo-mechanical (ECM) physics was established to understand the stress measurement. A qualitative agreement was reached between modeling and experimental data, and the critical size effect could be explained by the Li diffusive flux as governed by competition between the Li concentration and hydrostatic stress gradients. Below the critical size, the stress gradient drives Li toward the edges, where the electrode volume is free to expand, while above the critical size, the stress plateau inhibits Li diffusion to the edge and forces destructive stress relief by cracking. Furthermore, this work represents a promising methodology for in situ characterization of ECM coupling in battery electrodes, with suggestions provided for further improvement.

25 ENERGY STORAGE↗
NASA NTRS2019

Evaluation of Brightness Temperature Sensitivity to Snowpack Physical Properties Using Coupled Snow Physics and Microwave Radiative Transfer Models

There are multiple existing microwave radiative transfer models (RTMs) to simulate the brightness temperature (Tb) of snowpacks. It is still challenging to have consistent Tb responses from RTMs due to individual physical formulations of the snowpack scattering process. This article examines three of the widely-used multi-layer RTMs: 1) the microwave emission model of layered snowpacks (MEMLS); 2) the dense media radiative transfer based on the quasi-crystalline approximation (QCA) of Mie scattering of densely packed sticky spheres (DMRTQMS); and 3) the Helsinki University of Technology (HUT) model. Interestingly, these models yield slightly different Tb responses when driven by the same physical snowpack properties. Tb variations, dependent on the choice of RTMs, are then evaluated to improve the understanding of model differences in microwave emission from a snowpack. We first perform a sensitivity study of the Tb predictions from the three RTMs as a function of snow grain sizes, densities, and depths. While Tb from all three RTMs decreases with increasing snow grain sizes, it is found that a scaling factor is required to have the same amount of Tb attenuation for small grain sizes within the Rayleigh scattering regime. For larger grain sizes, however, a scaling coefficient is not enough to match the model outputs due to the different scattering assumptions of the RTMs. For a single snow layer with increasing snow depths and densities, all three RTMs exhibit Tb attenuations arising from the increase in path lengths and optical depths. Further evaluations are conducted by feeding the three RTMs with the output of a snow physics model driven by in situ weather forcing in a coupled simulation. Outputs of this coupled model include snowpack physical properties and Tbs. By using snow stratigraphy observations, another set of Tb simulation is also conducted with RTMs driven by in situ snowpit observations. The snow physics outputs from the coupled case are compared against in situ snow stratigraphy observations.

Do Hyuk Kang↗
DOE OSTI2022

Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling

Abstract Effective plasma transport modeling of magnetically confined fusion devices relies on having an accurate understanding of the ion composition and radiative power losses of the plasma. Generally, these quantities can be obtained from solutions of a collisional-radiative (CR) model at each time step within a plasma transport simulation. However, even compact, approximate CR models can be computationally onerous to evaluate, and in-situ evaluation of these models within a larger plasma transport code can lead to a rigid bottleneck. As a way to bypass this bottleneck, we propose deploying artificial neural network (ANN) surrogates to allow rapid evaluation of the necessary plasma quantities. However, one issue with training an accurate ANN surrogate is the reliance on a sufficiently large and representative training and validation data set, which can be time-consuming to generate. In this work we explore a data-driven active learning and training routine to allow autonomous adaptive sampling of the problem parameter space to ensure a sufficiently large and meaningful set of training data is assembled for the network training. As a result, we can demonstrate approximately order-of-magnitude savings in required training data samples to produce an accurate surrogate.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2024

A Survey of Modeling Activities by NASA’s Orbital Debris Program Office

NASA’s Orbital Debris Program Office (ODPO) develops and maintains a number of modeling tools to analyze and simulate the orbital debris environment. One of the most important products produced by the NASA ODPO is the Orbital Debris Engineering Model (ORDEM). This model can be used by satellite designers and operators to design missions for better protection against the debris environment. The ODPO is currently working on the next generation, designated ORDEM 4.0. ORDEM 4.0 will include many known features from previous models, such as the ability to input a spacecraft orbit and time and the ability to compute the flux as a function of debris size, impact speed, impact direction, and debris material densities, as well as uncertainty information on the flux. A new addition will be a parameterized debris shape model based on laboratory hypervelocity impact tests, including DebriSat. ORDEM is primarily based on dedicated debris measurements, such as by the Haystack Ultrawideband Satellite Imaging Radar (HUSIR), NASA’s Goldstone radar, and observations of geosynchronous orbits (GEO) using the Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT). In addition to ORDEM, the ODPO also maintains other models, such as the LEO-to-GEO Environment Debris (LEGEND) model for studies of long-term evolution of Earth’s debris environment, with the ability to study various mitigation and remediation strategies. Another model, the Satellite Breakup Risk Assessment Model (SBRAM), is used to analyze how satellite breakups may affect critical space missions (such as the ISS) on short notice. In addition to these models, the ODPO maintains other secondary models used to model satellite explosions and collisions, analyze radar, optical, and in situ data, and to model such things as solar activity and orbit evolution. In this presentation, a survey of these models will be presented, showing how the different models are used together to create a comprehensive picture of Earth’s debris environment.

Mark Matney↗
NASA NTRS1991

Low-frequency variability of sea level as related to the heat balance of global oceans

The TOPEX/POSEIDON mission will determine global changes of sea level with unprecedented accuracy. Our main objective is the use TOPEX/POSEIDON data, concurrent in situ ocean measurements, and other satellite observations to document and diagnose physical processes by which heat is exchanged with the atmosphere, stored in the ocean, or transported by ocean circulation. During the prelaunch period, our objectives are to advise the project on an improved method of retrieving sea level data and prepare for the application of TOPEX/POSEIDON data by developing a diagnostic model using in situ measurements and altimeter observations from Geosat and the European Remote Sensing satellite.

Liu, W. Timothy↗
DOE OSTI2026

Integration of the Biot–Gassmann Fluid Substitution Method and Machine Learning-Based Velocity–Stress Relationship for Estimating In Situ Stresses

Recent advancements have shown that in situ stresses can be reliably estimated through an integrated machine/deep learning (ML/DL)-based framework, which relies on models trained and validated using true triaxial ultrasonic velocity (TUV) experimental data that involve measurements of ultrasonic velocity in saturated rocks under varying stress configurations. However, when the goal is to interpret lower frequency measurements, it may be more appropriate to run experiments on dry rocks and then obtain Biot–Gassmann-derived equivalent saturated velocities (low-frequency approximation) and employ these quantities for training ML/DL models to predict in situ stress. Whether the dispersion effect of frequency on the velocity–stress relationship substantially impacts in situ stress prediction is an important and unresolved question. This work presents an enhancement of ML/DL-based workflow by training and implementing ML/DL models using equivalent saturated acoustic velocities (low-frequency) obtained by applying Biot–Gassmann fluid substitution on the ultrasonic velocities of dry cores. The models were trained on TUV data sets derived from three subsurface cores extracted from the geothermal well 16B(78)-32 at the Utah FORGE site. Each core was subjected to 75 unique stress configurations for velocity measurement in the dry state. The ML/DL trained on the TUV data set with equivalent saturated velocities demonstrated promising performance to predict in situ stress in subsurface geological rocks using velocity–stress relationships with R 2 of 0.86, 0.971, and 0.975 and root mean squared error (RMSE) of 2.59, 1.92, and 1.80 for validation/testing phases of vertical, minimum horizontal, and maximum horizontal stress models, respectively. Additionally, interpretation and explanation by Shapley additive explanations (SHAP) analysis further improved scientific validation and model reliability for estimating in situ stresses.

colloids↗
NASA NTRS2018

Evaluation of a High-Resolution Numerical Weather Prediction Model’s Simulated Clouds Using Observations from CloudSat, GOES-13 and in Situ Aircraft

This study aimed to assess tropical cloud properties predicted by Environment and Climate Change Canada’s Global Environmental Multiscale (GEM)modelwhen run with the Milbrandt–Yau double-moment cloud microphysical scheme and one-way nesting that culminated at a (∼300 km)2 inner domain with 0.25 km horizontal grid spacing. The assessment utilized satellite and in situ data collected during the High Ice Water Content (HIWC) and High Altitude Ice Crystals (HAIC) projects for a mesoscale convective system on 16May 2015 over French Guiana.Data fromCloud- Sat’s cloud-profiling radar and GOES-13’s imager were compared to data either simulated directly by GEM or produced by operating on GEM’s cloud data with both the CFMIP (Cloud Feedback Model Intercomparison Project) Observation Simulator Package (COSP) instrument simulator and a three-dimensional Monte Carlo solar radiative transfer model. In situ observations were made from research aircraft – Canada’s National Research Council Convair-580 and the French SAFIRE Falcon-20 – whose flight paths were aligned with CloudSat’s ground-track. Spatial and temporal shifts of clouds simulated by GEM compared well to GOES-13 imagery. There are, however, differences between simulated and observed amounts of high and low cloud. While GEM did well at predicting ranges of ice-water content (IWC) near 11 km altitude (Falcon-20), it produces too much graupel and snow near 7 km (Convair-580). This produced large differences between CloudSat’s and COSP-generated radar reflectivities and two-way attenuations. On the other hand, CloudSat’s inferred values of IWC agree well with in situ samples at both altitudes. Generally, GEM’s visible reflectances exceeded GOES-13’s on account of having produced too much low-level liquid cloud. It is expected that GEM’s disproportioning of cloud hydrometeors will improve once it includes a better representation of secondary ice production.

Qu, Zhipeng↗
NASA NTRS2008

Tolerance and UQ4SIM: Nimble Uncertainty Documentation and Analysis Software

Ultimately, scientific numerical models need quantified output uncertainties so that modeling can evolve to better match reality. Documenting model input uncertainties and variabilities is a necessary first step toward that goal. Without known input parameter uncertainties, model sensitivities are all one can determine, and without code verification, output uncertainties are simply not reliable. The basic premise of uncertainty markup is to craft a tolerance and tagging mini-language that offers a natural, unobtrusive presentation and does not depend on parsing each type of input file format. Each file is marked up with tolerances and optionally, associated tags that serve to label the parameters and their uncertainties. The evolution of such a language, often called a Domain Specific Language or DSL, is given in [1], but in final form it parallels tolerances specified on an engineering drawing, e.g., 1 +/- 0.5, 5 +/- 10%, 2 +/- 10 where % signifies percent and o signifies order of magnitude. Tags, necessary for error propagation, can be added by placing a quotation-mark-delimited tag after the tolerance, e.g., 0.7 +/- 20% 'T_effective'. In addition, tolerances might have different underlying distributions, e.g., Uniform, Normal, or Triangular, or the tolerances may merely be intervals due to lack of knowledge (uncertainty). Finally, to address pragmatic considerations such as older models that require specific number-field formats, C-style format specifiers can be appended to the tolerance like so, 1.35 +/- 10U_3.2f. As an example of use, consider figure 1, where a chemical reaction input file is has been marked up to include tolerances and tags per table 1. Not only does the technique provide a natural method of specifying tolerances, but it also servers as in situ documentation of model uncertainties. This tolerance language comes with a utility to strip the tolerances (and tags), to provide a path to the nominal model parameter file. And, as shown in [1], having the ability to quickly mark and identify model parameter uncertainties facilitates error propagation, which in turn yield output uncertainties.

Kleb, Bil↗
NASA NTRS2001

Ice Nucleation in Deep Convection

The processes controlling production of ice crystals in deep, rapidly ascending convective columns are poorly understood due to the difficulties involved with either modeling or in situ sampling of these violent clouds. A large number of ice crystals are no doubt generated when droplets freeze at about -40 C. However, at higher levels, these crystals are likely depleted due to precipitation and detrainment. As the ice surface area decreases, the relative humidity can increase well above ice saturation, resulting in bursts of ice nucleation. We will present simulations of these processes using a large-eddy simulation model with detailed microphysics. Size bins are included for aerosols, liquid droplets, ice crystals, and mixed-phase (ice/liquid) hydrometers. Microphysical processes simulated include droplet activation, freezing, melting, homogeneous freezing of sulfate aerosols, and heterogeneous ice nucleation. We are focusing on the importance of ice nucleation events in the upper part of the cloud at temperatures below -40 C. We will show that the ultimate evolution of the cloud in this region (and the anvil produced by the convection) is sensitive to these ice nucleation events, and hence to the composition of upper tropospheric aerosols that get entrained into the convective column.

Jensen, Eric↗