Search NASA⌕ Search

SEARCH · Search NASA

Results for “agent based modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Office of Equal Opportunity Programs

The NASA Glenn Office of Equal Opportunity Programs works to provide quality service for all programs and/or to assist the Center in becoming a model workplace. During the summer of 2004, I worked with Deborah Cotleur along with other staff members to create and modify customer satisfaction surveys. This office aims to assist in developing a model workplace by providing functions as a change agent to the center by serving as an advisor to management to ensure equity throughout the Center. In addition, the office serves as a mediator for the Center in addressing issues and concerns. Lastly, the office provides assistance to employees to enable attainment of personal and organizational goals. The Office of Equal Opportunities is a staff office which reports and provides advice to the Center Director and Executive Leadership, implements laws, regulations, and presidential executive orders, and provides center wide leadership and assistance to NASA GRC employees. Some of the major responsibilities of the office include working with the discrimination complaints program, special emphasis programs (advisory groups), management support, monitoring and evaluation, contract compliance, and community outreach. During my internship in this office, my main objective was to create four customer satisfaction surveys based on EO retreats, EO observances, EO advisory boards, and EO mediation/counseling. I created these surveys after conducting research on past events and surveys as well as similar survey research created and conducted by other NASA centers, program for EO Advisory group members, leadership training sessions for supervisors, preventing sexual harassment training sessions, and observance events. I also conducted research on the style and format from feedback surveys from the Marshall Equal Opportunity website, the Goddard website, and the main NASA website. Using the material from the Office of Equal Opportunity Programs at Glenn Research Center along with my previous research, I created four customer satisfaction surveys. Additional information is included in the original extended abstract.

Chin, Jennifer L.↗
DOE OSTI2017

CG13250, a novel bromodomain inhibitor, suppresses proliferation of multiple myeloma cells in an orthotopic mouse model

Multiple myeloma (MM) is characterized by the clonal proliferation of neoplastic plasma cells. Despite a stream of new molecular targets based on better understanding of the disease, MM remains incurable. Epigenomic abnormalities contribute to the pathogenesis of MM. bromodomain 4 (BRD4), a member of the bromodomain and extraterminal (BET) family, binds to acetylated histones during M/G1 transition in the cell cycle promoting progression to S phase. In this study, we investigated the effects of a novel BET inhibitor CG13250 on MM cells. CG13250 inhibited ligand binding to BRD4 in a dose-dependent manner and with an IC{sub 50} value of 1.1 μM. It inhibited MM proliferation in a dose-dependent manner and arrested cells in G1, resulting in the induction of apoptosis through caspase activation. CG13250 inhibited the binding of BRD4 to c-MYC promoter regions suppressing the transcription of the c-MYC gene. Administered in vivo, CG13250 significantly prolonged survival of an orthotopic MM-bearing mice. In conclusion, CG13250 is a novel bromodomain inhibitor that is a promising molecular targeting agent against MM. - Highlights: • A novel bromodomain inhibitor CG13250 suppresses MM cell proliferation. • CG13250 decreases C-MYC expression, resulting in the induction of apoptosis. • CG13250 prolongs the survivals of MM-bearing mice.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2003

NASA Tech Briefs, October 2003

Topics covered include: Cryogenic Temperature-Gradient Foam/Substrate Tensile Tester; Flight Test of an Intelligent Flight-Control System; Slat Heater Boxes for Thermal Vacuum Testing; System for Testing Thermal Insulation of Pipes; Electrical-Impedance-Based Ice-Thickness Gauges; Simulation System for Training in Laparoscopic Surgery; Flasher Powered by Photovoltaic Cells and Ultracapacitors; Improved Autoassociative Neural Networks; Toroidal-Core Microinductors Biased by Permanent Magnets; Using Correlated Photons to Suppress Background Noise; Atmospheric-Fade-Tolerant Tracking and Pointing in Wireless Optical Communication; Curved Focal-Plane Arrays Using Back-Illuminated High-Purity Photodetectors; Software for Displaying Data from Planetary Rovers; Software for Refining or Coarsening Computational Grids; Software for Diagnosis of Multiple Coordinated Spacecraft; Software Helps Retrieve Information Relevant to the User; Software for Simulating a Complex Robot; Software for Planning Scientific Activities on Mars; Software for Training in Pre-College Mathematics; Switching and Rectification in Carbon-Nanotube Junctions; Scandia-and-Yttria-Stabilized Zirconia for Thermal Barriers; Environmentally Safer, Less Toxic Fire-Extinguishing Agents; Multiaxial Temperature- and Time-Dependent Failure Model; Cloverleaf Vibratory Microgyroscope with Integrated Post; Single-Vector Calibration of Wind-Tunnel Force Balances; Microgyroscope with Vibrating Post as Rotation Transducer; Continuous Tuning and Calibration of Vibratory Gyroscopes; Compact, Pneumatically Actuated Filter Shuttle; Improved Bearingless Switched-Reluctance Motor; Fluorescent Quantum Dots for Biological Labeling; Growing Three-Dimensional Corneal Tissue in a Bioreactor; Scanning Tunneling Optical Resonance Microscopy; The Micro-Arcsecond Metrology Testbed; Detecting Moving Targets by Use of Soliton Resonances; and Finite-Element Methods for Real-Time Simulation of Surgery.

Source record↗
NASA NTRS2005

Predictive Interfaces for Long-Distance Tele-Operations

We address the development of predictive tele-operator interfaces for humanoid robots with respect to two basic challenges. Firstly, we address automating the transition from fully tele-operated systems towards degrees of autonomy. Secondly, we develop compensation for the time-delay that exists when sending telemetry data from a remote operation point to robots located at low earth orbit and beyond. Humanoid robots have a great advantage over other robotic platforms for use in space-based construction and maintenance because they can use the same tools as astronauts do. The major disadvantage is that they are difficult to control due to the large number of degrees of freedom, which makes it difficult to synthesize autonomous behaviors using conventional means. We are working with the NASA Johnson Space Center's Robonaut which is an anthropomorphic robot with fully articulated hands, arms, and neck. We have trained hidden Markov models that make use of the command data, sensory streams, and other relevant data sources to predict a tele-operator's intent. This allows us to achieve subgoal level commanding without the use of predefined command dictionaries, and to create sub-goal autonomy via sequence generation from generative models. Our method works as a means to incrementally transition from manual tele-operation to semi-autonomous, supervised operation. The multi-agent laboratory experiments conducted by Ambrose et. al. have shown that it is feasible to directly tele-operate multiple Robonauts with humans to perform complex tasks such as truss assembly. However, once a time-delay is introduced into the system, the rate of tele\ioperation slows down to mimic a bump and wait type of activity. We would like to maintain the same interface to the operator despite time-delays. To this end, we are developing an interface which will allow for us to predict the intentions of the operator while interacting with a 3D virtual representation of the expected state of the robot. The predictive interface anticipates the intention of the operator, and then uses this prediction to initiate appropriate sub-goal autonomy tasks.

Wheeler, Kevin R.↗
DOE OSTI2017

Implementation of Energy Code Controls Requirements in New Commercial Buildings

Most state energy codes in the United States are based on one of two national model codes; ANSI/ASHRAE/IES 90.1 (Standard 90.1) or the International Code Council (ICC) International Energy Conservation Code (IECC). Since 2004, covering the last four cycles of Standard 90.1 updates, about 30% of all new requirements have been related to building controls. These requirements can be difficult to implement and verification is beyond the expertise of most building code officials, yet the assumption in studies that measure the savings from energy codes is that they are implemented and working correctly. The objective of the current research is to evaluate the degree to which high impact controls requirements included in commercial energy codes are properly designed, commissioned and implemented in new buildings. This study also evaluates the degree to which these control requirements are realizing their savings potential. This was done using a three-step process. The first step involved interviewing commissioning agents to get a better understanding of their activities as they relate to energy code required controls measures. The second involved field audits of a sample of commercial buildings to determine whether the code required control measures are being designed, commissioned and correctly implemented and functioning in new buildings. The third step includes compilation and analysis of the information gather during the first two steps. Information gathered during these activities could be valuable to code developers, energy planners, designers, building owners, and building officials.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
DOE OSTI2024

The safety of magnetic resonance imaging contrast agents

Gadolinium-based contrast agents are increasingly used in clinical practice. While these pharmaceuticals are verified causal agents in nephrogenic systemic fibrosis, there is a growing body of literature supporting their role as causal agents in symptoms associated with gadolinium exposure after intravenous use and encephalopathy following intrathecal administration. Gadolinium-based contrast agents are multidentate organic ligands that strongly bind the metal ion to reduce the toxicity of the metal. The notion that cationic gadolinium dissociates from these chelates and causes the disease is prevalent among patients and providers. We hypothesize that non-ligand-bound (soluble) gadolinium will be exceedingly low in patients. Soluble, ionic gadolinium is not likely to be the initial step in mediating any disease. The Kidney Institute of New Mexico was the first to identify gadolinium-rich nanoparticles in skin and kidney tissues from magnetic resonance imaging contrast agents in rodents. In 2023, they found similar nanoparticles in the kidney cells of humans with normal renal function, likely from contrast agents. We suspect these nanoparticles are the mediators of chronic toxicity from magnetic resonance imaging contrast agents. This article explores associations between gadolinium contrast and adverse health outcomes supported by clinical reports and rodent models.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2017

Drivers of Precipitation Change: An Energetic Understanding

The response of the hydrological cycle to climate forcings can be understood within the atmospheric energy budget framework. In this study precipitation and energy budget responses to five forcing agents are analyzed using 10 climate models from the Precipitation Driver Response Model Intercomparison Project (PDRMIP). Precipitation changes are split into a forcing-dependent fast response and a temperature-driven hydrological sensitivity. Globally, when normalized by top-of-atmosphere (TOA) forcing, fast precipitation changes are most sensitive to strongly absorbing drivers (CO2, black carbon). However, over land fast precipitation changes are most sensitive to weakly absorbing drivers (sulfate, solar) and are linked to rapid circulation changes. Despite this, land-mean fast responses to CO2 and black carbon exhibit more intermodel spread. Globally, the hydrological sensitivity is consistent across forcings, mainly associated with increased longwave cooling, which is highly correlated with intermodel spread. The land-mean hydrological sensitivity is weaker, consistent with limited moisture availability. The PDRMIP results are used to construct a simple model for land-mean and sea-mean precipitation change based on sea surface temperature change and TOA forcing. The model matches well with CMIP5 ensemble mean historical and future projections, and is used to understand the contributions of different drivers. During the twentieth century, temperature-driven intensification of land-mean precipitation has been masked by fast precipitation responses to anthropogenic sulfate and volcanic forcing, consistent with the small observed trend. However, as projected sulfate forcing decreases, and warming continues, land-mean precipitation is expected to increase more rapidly, and may become clearly observable by the mid-twenty-first century.

Richardson, T. B.↗
NASA NTRSDate not supplied

Synthetic Data Generation for 3D Mesh Prediction and Spatial Reasoning During Multi-Agent Robotic Missions

In-space assembly operations require accurate reasoning over the pose, location, and structural organization of both the autonomous agents and assembly materials. In a full six-degree-of-freedom space, an accurate understanding of the full three-dimensional structure of the object of interest greatly enriches information for pose estimation and collision planning. Current methods of predicting pose estimation require a priori understanding of the shape of the object. Additionally, visual information in the space environment is impacted by variations in contrast and illumination. Using synthetic data allows us to rapidly generate large datasets with in varying environments and lighting conditions.This work details the generation of synthetic data used to explore the use of a region-based convolutional neural networks to detect objects of interest and predict a voxel-based three-dimensional mesh in order to understand their full three-dimensional shape. This mesh provides useful spatial information during in-space assembly operations without requiring either the complexity of maintaining models over the progress of building an object or observations from multiple angles. The generated meshes are then compared to that of ground truth in order to measure its performance.

synthetic data↗
DOE OSTI2019

Synthesis, properties, and antianemic activity of new metal complexes of sodium pectinate with iron and calcium

A new water-soluble complex based on sodium pectinate with iron and calcium (P-NaFeCa) was synthesized. The regularities of the complexation of sodium pectinate (with a 35% content of COONa groups) with biogenic metals Ca and Fe were studied. The content of macro- and microelements was determined by atomic emission spectrometry; the product was identified by IR spectroscopy. Using atomic force microscopy, it was found that the average particle size of P-NaFeCa is ~170 nm. The efficiency of P-NaFeCa (per os) was studied for the first time in animals with hemolytic anemia modeled by administration of phenylhydrazine. It was experimentally shown that in vivo P-NaCaFe at a dose of 60 mg kg{sup –1} containing 50% of the recommended therapeutic dose of iron helps to recover the number of erythrocytes and mean concentration hemoglobin in erythrocyte, which indicates the efficiency of P-NaCaFe as an agent restoring the blood parameters after exposure to hemolytic poisons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Synthetic Data Generation for 3D Mesh Prediction and Spatial Reasoning During Multi-Agent Robotic Missions

In-space assembly operations require accurate reasoning over the pose, location, and structural organization of both the autonomous agents and assembly materials. In a full six-degree-of-freedom space, an accurate understanding of the full three-dimensional structure of the object of interest greatly enriches information for pose estimation and collision planning. Current methods of predicting pose estimation require a priori understanding of the shape of the object. Additionally, visual information in the space environment is impacted by variations in contrast and illumination. Using synthetic data allows us to rapidly generate large datasets with in varying environments and lighting conditions. This work details the generation of synthetic data used to explore the use of a region-based convolutional neural networks to detect objects of interest and predict a voxel-based three-dimensional mesh in order to understand their full three-dimensional shape. This mesh provides useful spatial information during in-space assembly operations without requiring either the complexity of maintaining models over the progress of building an object or observations from multiple angles. The generated meshes are then compared to that of ground truth in order to measure its performance.

James Ecker↗
DOE OSTI2024

Characterization of two affinity matured Anti-Yersinia pestis F1 human antibodies with medical countermeasure potential

Yersinia pestis , the causative agent of plague and a biological threat agent, presents an urgent need for novel medical countermeasures due to documented cases of naturally acquired antibiotic resistance and potential person-to-person spread during a pneumonic infection. Immunotherapy has been proposed as a way to circumvent current and future antibiotic resistance. Here, we describe the development and characterization of two affinity matured human antibodies (αF1Ig AM2 and αF1Ig AM8) that promote survival of mice after exposure to aerosolized Y . pestis . We share details of the error prone PCR and yeast display technology-based affinity maturation process that we used. The resultant matured antibodies have nanomolar affinity for Y . pestis F1 antigen, are produced in high yield, and are resilient to 37°C stress for up to 6 months. Importantly, in vitro assays using a murine macrophage cell line demonstrated that αF1Ig AM2 and αF1Ig AM8 are opsonic. Even more importantly, in vivo studies using pneumonic plague mouse models showed that 100% of the mice receiving 500 μg of IgGs αF1Ig AM2 and αF1Ig AM8 survived lethal challenge with aerosolized Y . pestis CO92. Combined, these results provide evidence of the quality and robustness of αF1Ig AM2 and αF1Ig AM8 and support their development as potential medical countermeasures against plague.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Tensorized Interior Radiative Heat Transfer for a Scalable and Calibrated Building Energy Simulator

Building energy simulation is a critical tool for developing and testing advanced control strategies, such as Reinforcement Learning (RL), to provide demand flexibility and affordable energy costs. The recently introduced Smart Buildings Control Suite (sbsim) provides a lightweight, scalable, and data-calibrated simulation environment based on a 2D finite-difference model. However, the initial model primarily focused on conductive and convective heat transfer, neglecting the significant impact of long-wave radiative heat exchange between interior surfaces. This paper presents a significant extension to the sbsim framework by incorporating a physically-grounded model for interior radiative heat transfer. Our primary contribution is the development and integration of a fully tensorized radiative heat transfer module, which preserves the computational efficiency and scalability of the original simulator. This was achieved by developing a pipeline for view factor calculation, including an algorithm to identify directly seeing surfaces within complex floor plans, and formulating the net radiation equations for efficient execution on modern hardware accelerators. We validate the numerical accuracy of our tensorized implementation by comparing its results against a traditional iterative approach, demonstrating identical outcomes. This enhancement increases the physical fidelity of sbsim, enabling more accurate training of RL agents for building energy optimization.

Ham, Sang woo↗
DOE OSTI2017

Elongation affinity, activation barrier, and stability of Aβ42 oligomers/fibrils in physiological saline

Amyloid-beta (Aβ) peptides, Aβ40 and the more neurotoxic Aβ42, have been the subject of many research efforts for Alzheimer's disease. In two recent independent investigations, the atomistic structure of Aβ42 fibril has been clearly established in the S-shaped conformation consisting of three β-sheets stabilized by salt bridges formed between the Lys28 sidechain and the C-terminus of Ala42. This structure distinctively differs from the long-known structure of Aβ40 in the β-hairpin shaped conformation consisting of two β-sheets. Recent in silico investigations based on all-atom models have reached closer agreement with the in vitro measurements of Aβ40 thermodynamics. In this study, we present an in silico investigation of Aβ42 thermodynamics. Using the established force field parameters in seven sets of all-atom simulations, we examined the stability of small Aβ42 oligomers in physiological saline. We computed the elongation affinity of the S-shaped Aβ42 fibril, reaching agreement with the experimental data. We also estimated the Arrhenius activation barrier along the elongation pathway (from the disordered conformation of a free Aβ42 peptide to its S-shaped conformation on a fibril) that amounts to about 16 kcal/mol, which is consistent with the experimental data. Based on these quantitative agreements, we conclude that aggregation of Aβ42 peptides into fibrils is thermodynamically slow without precipitation by extrinsic factors such as heparan sulfate proteoglycan and highlight the possibility to prevent Aβ42 aggregation by eliminating some precipitation factors or by increasing competitive agents to capture and transport free Aβ42 peptides from the cerebrospinal fluid. - Highlights: • Aβ42 fibril elongation affinity was computed with chemical accuracy, in agreement with in vitro data. • Small Aβ42 oligomers were found to be stable in physiological saline. • A long Aβ42 fibril-filament was shown to stabilize an Aβ dimer in its proximity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds

Abstract Morphological profiling is an omics-based approach for predicting intracellular targets of chemical compounds in which the dose-dependent morphological changes induced by the compound are systematically compared to the morphological changes in gene-deleted cells. In this study, we developed a reliable high-throughput (HT) platform for yeast morphological profiling using drug-hypersensitive strains to minimize compound use, HT microscopy to speed up data generation and analysis, and a generalized linear model to predict targets with high reliability. We first conducted a proof-of-concept study using six compounds with known targets: bortezomib, hydroxyurea, methyl methanesulfonate, benomyl, tunicamycin, and echinocandin B. Then we applied our platform to predict the mechanism of action of a novel diferulate-derived compound, poacidiene. Morphological profiling of poacidiene implied that it affects the DNA damage response, which genetic analysis confirmed. Furthermore, we found that poacidiene inhibits the growth of phytopathogenic fungi, implying applications as an effective antifungal agent. Thus, our platform is a new whole-cell target prediction tool for drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

Gravitational Effects on Distortion in Sintering

During sintering a powder compact gains strength through low-temperature interparticle bonding, usually induced by solid-state surface diffusion, followed by further strength contributions from high-temperature densification. In cases where a liquid phase forms, sintering densification is accelerated and shape retention is sustained while open pores remain and contribute capillary forces. Unfortunately, sintering densification requires the compact become thermally softened to a point where creep strain rates reach levels near 10(exp -2)/s when the liquid forms. On the other hand, thermal softening of the powder compact substantially reduces the strength at high temperatures. Therefore, the in situ strength evolution during sintering is a primary focus to separate compact densification (as required for high performance) with minimized distortion (as required for net-shaping). With respect to gravitation effects on distortion during sintering there are two points of substantial weakness - prior to significant interparticle bonding and during final pore closure. This research is focused on understanding the competition among interparticle neck growth, densification, thermal softening, grain boundary wetting, capillary effects associated with liquid wetting and residual porosity, and gravity. Most surprising is the apparent role of gravity, where the deviatoric stress acting on the powder structure induces skeletal formation that reduces distortion. In contrast with theory, microgravity samples exhibit more distortion yet fail to fully densify. Results are presented on the experimental concepts supporting an emerging model of sintering strength evolution that enables understanding of both distortion and densification. The experiments have relied on tungsten heavy alloys, various combinations of dihedral angle, pore size, initial porosity, liquid:solid ratio, and heating rates. On Earth, the dominant factor with respect to distortion is the starting body heterogeneity. Current modeling efforts are seeking some means to uniformly predict the distortion based on a starting pore structure heterogeneity parameter. Densification is largely unaffected by the initial pore structure, but distortion is highly variable, suggesting that nonuniform pore closure might be a significant parameter during sintering. With respect to flight experiments, plans include removal of the solid body forces acting on the solid grains, allowing stabilization of the pore structure and examination of the buoyancy effects with regard to distortion. In microgravity there is the surprising result that compacts do not densify, yet distort a factor that is contrary to all current sintering models. Densification without distortion during liquid phase sintering was achieved by manipulating microstructure and its evolution during sintering. Microstructure parameters such as the solid volume fraction, dihedral angle, initial porosity, and pore size were varied to measure densification and distortion behavior during LPS using W-Ni-Cu alloys. Green compacts were formed using ethylene-bis-stearamide as a pore-forming agent with the amount of polymer controlling the initial porosity. Different initial pore sizes were generated by varying the polymer particle size. Dihedral angle was varied by changing the Ni:Cu ratio in the alloys. Finally, the solid volume fraction was adjusted via the tungsten content. Distortion was quantified using profiles determined with a coordinate measuring machine to calculate a distortion parameter. Sintering results showed that solid volume fraction and dihedral angle are the dominant factors on densification and distortion during liquid phase sintering. Distortion decreases with increasing solid volume fraction and dihedral angle, while initial porosity and pore size have no observable effect on distortion at nearly full densification. Various strategies emerge to improve distortion control in liquid phase sintering.

German, Randall M.↗
NASA NTRS2013

Water Contents of the Mantle Beneath the Rio Grande Rift: FTIR Analysis of Kilbourne Hole Peridotite Xenoliths

Although nominally anhydrous mantle minerals contain only trace amounts of water, they are the main reservoir of water in the mantle. Added up at the scale of the Earth's mantle, these trace amounts of water represent oceans worth in mass]. Mantle xenoliths from Kilbourne Hole in southern New Mexico are ideal to study mantle water distribution in a rift tectonic setting as they come from a recently‐erupted maar in the middle of the Rio Grande Rift. Eleven lherzolites, one harzburgite, and one dunite are being analyzed for water contents by FTIR. The xenoliths will also be analyzed for major and trace element composition, Fe3+/Summation (Fe) ratios, and characterized petrologically. Olivines exhibit variable water contents with less water at the rims compared to the cores. This is probably due to H loss during decompression and xenolith transport by the host magma. Mantle water contents appear to have been primarily preserved in the core of the olivines, based on diffusion modeling of the typically plateau-shaped water content profiles across these grains. Water concentrations are in equilibrium between clino- and orthopyroxene, but olivine concentrations are typically not in equilibrium with those of either pyroxene. Lherzolites analyzed so far have water contents of 2‐12 ppm H2O in olivines, 125‐165 ppm H2O in orthopyroxenes, and 328‐447 ppm H2O in clinopyroxenes. These water contents are similar to, but with a narrower range, than those for the respective minerals in other continental peridotite xenoliths. The lherzolites have bulk-rock (BR) Al2O3 contents that range between 3.17 and 3.78 wt%, indicating similar degrees of partial melting, which could explain the narrow range of their pyroxene water contents. Primitive mantle normalized rare earth element (REE) profiles of the bulk lherzolites vary from light REE depleted to flat, with no significant differences between, nor relation to, their mineral water contents. Consequently, the metasomatic agents that enriched LREEs in these lherzolites were most likely water-poor. The harzburgite and the dunite have lower weight percent Al2O3 compared to the lherzolites (2.11% and 0.34% respectively) indicating higher degrees of melting. Their olivine water contents, however, are similar to those of the lherzolites. Moreover, no correlations are observed between pyroxene water contents and indices of melting or metasomatism between the lherzolite group, the harzburgite, and the dunite, although the latter has the lowest pyroxene water contents. More samples will be analyzed to determine if the water contents are controlled by melting, metasomatism, or a combination of the two in the Kilbourne Hole mantle.

Schaffer, Lillian A.↗