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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Industrial Processes Influenced by Gravity

In considering new directions for low gravity research with particular regard to broadening the number and types of industrial involvements, it is noted that transport phenomena play a vital role in diverse processes in the chemical, pharmaceutical, food, and biotech industries. Relatively little attention has been given to the role of gravity in such processes. Accordingly, numerous industrial processes and phenomena are identified which involve gravity and/or surface tension forces. Phase separations and mixing are examples that will be significantly different in low gravity conditions. A basis is presented for expanding the scope of the low gravity research program and the potential benefits of such research is indicated.

Ostrach, Simon↗
NASA NTRS2006

Processes Influencing Ozone Levels in Alaskan Forest Fires Plumes during Long-Range Transport over the North Atlantic

A case of long-range transport of a biomass burning plume from Alaska to Europe is analyzed using a Lagrangian approach. This plume was sampled several times in the free troposphere over North America, the North Atlantic and Europe by 3 different aircraft during the IGAC Lagrangian 2K4 experiment which was part of the ICARTT/ITOP measurement intensive in summer 2004. Measurements in the plume showed enhanced values of CO, VOCs and NOy, mainly in form of PAN. Observed O3 levels increased by 17 ppbv over 5 days. A photochemical trajectory model, CiTTyCAT, is used to examine processes responsible for the chemical evolution of the plume. The model was initialized with upwind data, and compared with downwind measurements. The influence of high aerosol loading on photolysis rates in the plume is investigated using in-situ aerosol measurements in the plume and lidar retrievals of optical depth as input into a photolysis code (Fast-J), run in the model. Significant impacts on photochemistry are found with a decrease of 18 percent in O3 production and 24 percent in O3 destruction over 5 days when including aerosols. The plume is found to be chemically active with large O3 increases attributed primarily to PAN decomposition during descent of the plume towards Europe. The predicted O3 changes are very dependent on temperature changes during transport, and also, on water vapor levels in the lower troposphere which can lead to O3 destruction. Simulation of mixing/dilution was necessary to reproduce observed pollutants level in the plume. Mixing was simulated using background concentrations from measurements in air masses in close proximity to the plume, and mixing timescales (averaging 6.25 days) were derived from CO changes. Observed and simulated O3/CO correlations in the plume are also compared in order to evaluate the photochemistry in the model. Observed slopes changed from negative to positive over 5 days. This change, which can be attributed largely to photochemistry, is well reproduced by multiple model runs even if slope values are slightly underestimated suggesting small underestimation of photochemical processes. The possible impact of this biomass burning plume on O3 levels in the Europe boundary layer is also examined by running the model for a further 5 days, and comparing with data collected at surface sites, such as Jungfraujoch, which showed small O3 increases and elevated CO levels. The model predicts significant changes in O3 over this 10 days due to photochemistry but the signal is largely lost due to the effects of dilution. However, measurement in several others BB plumes over Europe show that O3 impact of Alaskan fires can be punctually significant over Europe.

Real, E.↗
DOE OSTI2024

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Life and death in the soil microbiome: how ecological processes influence biogeochemistry

We report soil microorganisms shape global element cycles in life and death. Living soil microorganisms are a major engine of terrestrial biogeochemistry, driving the turnover of soil organic matter — Earth’s largest terrestrial carbon pool and the primary source of plant nutrients. Their metabolic functions are influenced by ecological interactions with other soil microbial populations, soil fauna and plants, and the surrounding soil environment. Remnants of dead microbial cells serve as fuel for these biogeochemical engines because their chemical constituents persist as soil organic matter. This non-living microbial biomass accretes over time in soil, forming one of the largest pools of organic matter on the planet. In this Review, we discuss how the biogeochemical cycling of organic matter depends on both living and dead soil microorganisms, their functional traits, and their interactions with the soil matrix and other organisms. With recent omics advances, many of the traits that frame microbial population dynamics and their ecophysiological adaptations can be deciphered directly from assembled genomes or patterns of gene or protein expression. Thus, it is now possible to leverage a trait-based understanding of microbial life and death within improved biogeochemical models and to better predict ecosystem functioning under new climate regimes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Infrared thermometry in high temperature materials processing: influence of liquid water and steam

Here, the capability of four infrared thermometry techniques (2-colour and 1-colour pyrometers, Pyrolaser and IR camera) was evaluated with respect to the impact of water or steam in the line of sight to determine temperature from two heat sources (blackbody calibration source and steel block inside the furnace). The influence of liquid water on the temperature readings was minimal when using 2-colour pyrometry due to comparable absorption coefficients of water for the measured wavelengths. The signals measured using both the Pyrolaser and the 1-colour pyrometer were decreased due to the partial absorption and resulted in an apparent temperature lower than the actual. Water readily absorbed the IR signal in the range of the IR camera operation, resulting in no signal whenever liquid water was present in the line of sight. Steam caused the most deviation and fluctuation of temperature readings for all techniques due to the large level of light scattering in addition to the absorption of the radiant energy. A technique was developed to determine the transmissivity (apparent emissivity) when water or steam is in the line of sight of measurement. An approximate correction to the measurements based on Planck’s law is discussed for both 2-colour and 1-colour pyrometers.

2-colour and 1-colour pyrometry↗
NASA NTRS2020

Condensation, Partial Melting and Evaporation Processes Influence the Bulk Compositions of Spinel-Cored Spherules in the CO3.1 Chondrite Miller Range 90019

Here we focus on spinel-cored spherule calcium-aluminum rich inclusions (CAI), dominantly ~75-80 microns in diameter in the CO3.1 chondrite Miller Range 90019, which make up ~ 12 % of the fine-grained CAIs in one thin section. Their mineralogical content ranges from rare grossite- and hibonite-bearing varieties, through perovskite-melilitebearing, to fassaite-bearing and finally anorthitebearing. Non-spherical CAIs have been divided into 4 other groups, defined based on mineralogical abundances. We also characterized a group of AOAs from this sample. No glass has been recognized in any inclusions. Some relatively evolved members (anorthite-, spinel- + fassaite-bearing) among the spherules are found engulfed in AOAs. We characterized the bulk compositions of ~145 CAIs and AOAs in this meteorite, derived from EDS-x-ray mapping of the inclusions. We determined bulk compositions both with and without Wark-Lovering rims (when present), which are largely composed of diopside  forsterite. The balance of the inclusions appear to have not been melted or partially melted, but rather they have textures that indicate they are condensates, often modified by extensive reaction with nebular gases. This presents the opportunity to examine effects on the bulk compositions of spherules resulting potentially from melting plus evaporation. Other aspects of this suite of refractory inclusions have been discussed in these abstracts. Oxygen isotope variations in one spherule were presented in [4]. The latter study showed a complex history of reaction with nebular gases possessing a variety of Oisotope compositions. Additional O isotopic studies of inclusions in this work are included in Mane et al.

Ross, D. K.↗
DOE OSTI2025

Influence of Process Parameter and Build Rate Variations on Defect Formation in Laser Powder Bed Fusion SS316L

Laser powder bed fusion (LPBF) is an additive manufacturing process that has gained interest for its material fabrication due to multiple advantages, such as the ability to print parts with small feature sizes, good mechanical properties, reduced material waste, etc. However, variations in the key process parameters in LPBF may result in the instantiation of porosity defects and variation in build rate. Particularly, volumetric energy density (VED) is a variable that encapsulates a number of those parameters and represents the amount of energy input from the laser source to the feedstock. VED has been traditionally used to inform the quality of the printed part but different values of VED are presented as optimal values for certain material systems. An optimal VED value can be maintained by changing the key process parameters so that various combinations yield a constant value. In this study, an optimal constant VED value is maintained while printing SS316L with variable key processing parameters. Porosity analysis is performed using optical microscopy, as well as X-ray computed tomography, to reveal the volume density and distribution of those pores. Two primary defect categories are identified, namely lack of fusion and porosity induced by balling defects. The findings indicate that, even at optimal VED, variations in process parameters can significantly influence defect type, underscoring the sensitivity of defect formation to the variation of these parameters. Furthermore, a minor change in the build rate, driven by adjustments in process parameters, was found to influence defect categories. These findings emphasize that fine tuning the process parameters and build rate is essential to minimize defects. Finally, fiducial marks have been identified as a source of unintentional porosity defects. These results enable the refinement of process parameters, ultimately optimizing LPBF to achieve enhanced material density and expedite the printing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A new multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential processes under process model and parametric uncertainty

Process-based models have been widely used for hydrologic modeling, and it is a common practice to use sensitivity analysis methods for excluding non-influential hydrologic processes from further investigation and/or model improvement. This study develops a new method called multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential system processes and parameters. MMADS is conceptually similar to the Morris method for addressing parametric uncertainty, but has a unique feature to address both process model uncertainty (i.e., a process may be represented by multiple process models) and process model parameter uncertainty (i.e., parameters associated with a process model are random). MMADS first evaluates absolute differences of a quantity of interest (i.e., a system model output) by varying process models and/or process model parameter values, and then calculates the mean and variance of the differences for investigating process influence. The mean measures overall influence of the process on the quantity of interest, and the variance estimates influence of nonlinear effects of the process and/or its interactions with other processes. MMADS is an extension of the Morris method from a parameter space to a joint parameter-model space for explicitly addressing both process model uncertainty and model parameter uncertainty. The performance of MMADS is evaluated by using two numerical experiments. One experiment is based on Sobol’s G*-function with ten product elements, and has analytical solutions of the MMADS mean and variance of absolute differences. The other experiment is for groundwater flow modeling which considers three processes (i.e., recharge, geology, and snowmelt) that interact with each other. Finally, results indicate that MMADS is computationally efficient and can identify non-influential processes of complex hydrological systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Land Processes Can Substantially Impact the Mean Climate State

Terrestrial processes influence the atmosphere by controlling land-to-atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally-informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Solidification in immiscible systems: The influence of processing conditions on resulting microstructures

Research carried out over the past two decades in which attempts have been made to produce desirable microstructures in immiscible alloys is reviewed. Factors affecting the formation of desirable microstructures in immiscible alloys are addressed and some of the techniques that have been successful in controlling these factors in order to produce useful structures are discussed. Some of the intended applications of these alloys require a dispersion of one phase in the other, while other applications favor the use of aligned, fibrous microstructures with continuity of both phases. The microstructures obtained in immiscible alloys are often strongly influenced by processing conditions. Perhaps the most well known effect is gravity induced sedimentation of the denser immiscible phase during solidification. This difficulty with sedimentation led to low gravity processing in attempts to produce dispersed microstructures. However, experimentation under low gravity conditions soon revealed that many other factors, including wetting of the crucible walls and surface tension driven flows, often resulted in complete separation and coalescence of the two immiscible phases. The production of aligned fibrous microstructures in hypermonotectic alloys appears to be even more difficult than the formation of a dispersion. Here, in addition to sedimentation and crucible wetting, convective and interfacial instabilities must be considered as well. Calculation of the limits of stability during directional solidification were carried out but require experimental verification.

Andrews, J. Barry↗
DOE OSTI2022

Process Interactions Can Change Process Ranking in a Coupled Complex System Under Process Model and Parametric Uncertainty

For a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total-effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total-effect parameter sensitivity index for one system model to total-effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total-effect process sensitivity index includes not only the first-order process sensitivity index for measuring the importance of individual processes but also higher-order indices that account for process interactions. The total-effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. Here, the total-effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*-functions with analytical solutions of first-order and total-effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second-order and higher-order process interactions, the first-order and total-effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first-order and total-effect process sensitivity indices to appreciate the difference between the first-order impact of a process alone and the overall total-effect impact of the process itself and its interactions with other processes on a model output.

54 ENVIRONMENTAL SCIENCES↗