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At least 271 records · Page 15

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NASA NTRS1984

Composition, Structure and Evolution of Uranian and Neptunian Satellites

Large uncertainties in the current estimated densities of all of these satellites prevent detailed modeling or predictions. Nevertheless, current evidence suggests that at least Titania and Oberon might have anomalously high densities of 2-39 cm(-3), possibly requiring almost ice-free hydrated silicates or formation in a CO-rich environment, implying presence of CO-clathrate and a small ice/rock ratio. Trition and the four largest satellites of Uranus are massive enough to have undergone significant accretional heating and early differentiation; NH3-H2O volcanism; partial outgassing of CO, N2, Ch4; formation of dark surficial deposits of carbon-rich material obtained by UV irradiation of outgassed material; and, at least in the cases of Ariel and Triton, a possibility of weak ongoing icy volcanic activity. Triton may be the largest captured body in the solar system, with an unusual history and composition, including the possibility of substantial liquid or solid nitrogen obtained from either primordial NH3 photolysis or clathrate decomposition.

Stevenson, D. J.↗

NASA NTRS2021

DGEN Aeropropulsion Research Turbofan Core/Combustor-Noise Measurements-Experiment and Modal Structure at Core-Nozzle Exit

Data from a recent core/combustor-noise source-diagnostic test utilizing a small turbo-fan engine are analyzed. The campaign continued the exploration begun in a baseline test, but with more extensive acoustic instrumentation. Both tests were aimed at developing a better understanding of propulsion-noise sources and their impact on the farfield noise signature, in order to enable improved turbofan noise-prediction methods and noise-mitigation techniques. Simultaneous high-data-rate acoustic measurements (93 channels in total) were obtained using a circumferential sensor array at the core-nozzle exit in conjunction with sideline and farfield microphone arrays for several relevant engine operational points. Measurements were repeated for different circumferential and sideline array configurations, as well as for redundancy. The unsteady pressure field at the core-nozzle exit is documented in detail. Previous work suggested that the±1azimuthal duct mode could be cut-on at this location, which would have implications for combustor-noise modeling and prediction. The modal decomposition of the combustor noise at the core-nozzle exit verifies this observation. Select farfield sound-pressure-level spectra are also presented.

Aeroacoustics↗

NASA NTRS1992

Intelligent robots for planetary exploration and construction

Robots capable of practical applications in planetary exploration and construction will require realtime sensory-interactive goal-directed control systems. A reference model architecture based on the NIST Real-time Control System (RCS) for real-time intelligent control systems is suggested. RCS partitions the control problem into four basic elements: behavior generation (or task decomposition), world modeling, sensory processing, and value judgment. It clusters these elements into computational nodes that have responsibility for specific subsystems, and arranges these nodes in hierarchical layers such that each layer has characteristic functionality and timing. Planetary exploration robots should have mobility systems that can safely maneuver over rough surfaces at high speeds. Walking machines and wheeled vehicles with dynamic suspensions are candidates. The technology of sensing and sensory processing has progressed to the point where real-time autonomous path planning and obstacle avoidance behavior is feasible. Map-based navigation systems will support long-range mobility goals and plans. Planetary construction robots must have high strength-to-weight ratios for lifting and positioning tools and materials in six degrees-of-freedom over large working volumes. A new generation of cable-suspended Stewart platform devices and inflatable structures are suggested for lifting and positioning materials and structures, as well as for excavation, grading, and manipulating a variety of tools and construction machinery.

Albus, James S.↗

NASA NTRS2001

Trioxane-Air Counterflow Diffusion Flames in Normal and Microgravity

Trioxane, a weakly bound polymer of formaldehyde (C3H6O3, m.p. 61 C, b.p. 115 C), is a uniquely suited compound for studying material flammability. Like many of the more commonly used materials for such tests (e.g., delrin, polyethylene, acrylic sheet, wood, and paper), it displays relevant phenomena (internal heat conduction, melting, vaporization, thermal decomposition, and gas phase reaction of the decomposition products). Unlike the other materials, however, it is non-sooting and has simple and well-known chemical kinetic pathways for its combustion. Hence it should prove to be much more useful for numerical modeling of surface combustion than the complex fuels typically used. We have performed the first exploratory tests of trioxane combustion in the counterflow configuration to determine its potential as a surrogate solid fuel which allows detailed modeling. The experiments were performed in the spring and summer of 1998 at the National Institute of Standards and Technology in Gaithersburg, MD, and at NASA-GRC in Cleveland. Using counterflow flames at 1-g, we measured the fuel consumption rate and the extinction conditions with added N2 in the air; at mg conditions, we observed the ignition characteristics and flame shape from video images. We have performed numerical calculations of the flame structure, but these are not described here due to space limitations. This paper summarizes some burning characteristics of trioxane relevant to its use for studying flame spread and fire suppression.

Linteris, Gregory T.↗

NASA NTRS1993

Efficient partitioning and assignment on programs for multiprocessor execution

The general problem studied is that of segmenting or partitioning programs for distribution across a multiprocessor system. Efficient partitioning and the assignment of program elements are of great importance since the time consumed in this overhead activity may easily dominate the computation, effectively eliminating any gains made by the use of the parallelism. In this study, the partitioning of sequentially structured programs (written in FORTRAN) is evaluated. Heuristics, developed for similar applications are examined. Finally, a model for queueing networks with finite queues is developed which may be used to analyze multiprocessor system architectures with a shared memory approach to the problem of partitioning. The properties of sequentially written programs form obstacles to large scale (at the procedure or subroutine level) parallelization. Data dependencies of even the minutest nature, reflecting the sequential development of the program, severely limit parallelism. The design of heuristic algorithms is tied to the experience gained in the parallel splitting. Parallelism obtained through the physical separation of data has seen some success, especially at the data element level. Data parallelism on a grander scale requires models that accurately reflect the effects of blocking caused by finite queues. A model for the approximation of the performance of finite queueing networks is developed. This model makes use of the decomposition approach combined with the efficiency of product form solutions.

Standley, Hilda M.↗

NASA NTRS2022

Detailing Cloud Property Feedbacks with a Regime-Based Decomposition

Diagnosing the root causes of cloud feedback in climate models and reasons for inter-model disagreement is a necessary first step in understanding their wide variation in climate sensitivities. Here we bring together two analysis techniques that illuminate complementary aspects of cloud feedback. The first quantifies feedbacks from changes in cloud amount, altitude, and optical depth, while the second separates feedbacks due to cloud property changes within specific cloud regimes from those due to regime occurrence frequency changes. We find that in the global mean, shortwave cloud feedback averaged across ten models comes solely from a positive within-regime cloud amount feedback countered slightly by a negative within-regime optical depth feedback. These within-regime feedbacks are highly uniform: In nearly all regimes, locations, and models, cloud amount decreases and cloud albedo increases with warming. In contrast, global-mean across-regime components vary widely across models but are very small on average. This component, however, is dominant in setting the geographic structure of the shortwave cloud feedback: Thicker, more extensive cloud types increase at the expense of thinner, less extensive cloud types in the extratropics, and vice versa at low latitudes. The prominent negative extratropical optical depth feedback has contributions from both within- and across-regime components, suggesting that thermodynamic processes affecting cloud properties as well as dynamical processes that favor thicker cloud regimes are important. The feedback breakdown presented herein may provide additional targets for observational constraints by isolating cloud property feedbacks within specific regimes without the obfuscating effects of changing dynamics that may differ across timescales.

climate sensitivity↗

NASA NTRSDate not supplied

Quantifying Emergent Fluid Dynamics Using Reynolds-Interpolated Fluid Reduced-order Models

Fluid reduced-order models (ROMs) which capture the flow physics within the problem's physical domain are usually constrained in accuracy to only the parameter points, e.g. Reynolds and Mach numbers, at which reference data was provided. Interpolation-focused quantity-of-interest ROMs are often structured differently and fail to provide flow volume data with the same quality - if at all. In this paper, techniques which reside at the intersection of these two ROM schools - flow physics ROMs which can be interpolated within a parameter space of interest - are explored. Using a combination of existing and novel techniques, emergent physics are identified using a fluid ROM at parameter points which are not provided in the ROM's training data.

uncertainty quantification↗

NASA NTRSDate not supplied

Quantifying Emergent Fluid Dynamics Using Reynolds-Interpolated Fluid Reduced-order Models

uncertainty quantification↗

NASA NTRS1999

Parallelization of the Physical-Space Statistical Analysis System (PSAS)

Atmospheric data assimilation is a method of combining observations with model forecasts to produce a more accurate description of the atmosphere than the observations or forecast alone can provide. Data assimilation plays an increasingly important role in the study of climate and atmospheric chemistry. The NASA Data Assimilation Office (DAO) has developed the Goddard Earth Observing System Data Assimilation System (GEOS DAS) to create assimilated datasets. The core computational components of the GEOS DAS include the GEOS General Circulation Model (GCM) and the Physical-space Statistical Analysis System (PSAS). The need for timely validation of scientific enhancements to the data assimilation system poses computational demands that are best met by distributed parallel software. PSAS is implemented in Fortran 90 using object-based design principles. The analysis portions of the code solve two equations. The first of these is the "innovation" equation, which is solved on the unstructured observation grid using a preconditioned conjugate gradient (CG) method. The "analysis" equation is a transformation from the observation grid back to a structured grid, and is solved by a direct matrix-vector multiplication. Use of a factored-operator formulation reduces the computational complexity of both the CG solver and the matrix-vector multiplication, rendering the matrix-vector multiplications as a successive product of operators on a vector. Sparsity is introduced to these operators by partitioning the observations using an icosahedral decomposition scheme. PSAS builds a large (approx. 128MB) run-time database of parameters used in the calculation of these operators. Implementing a message passing parallel computing paradigm into an existing yet developing computational system as complex as PSAS is nontrivial. One of the technical challenges is balancing the requirements for computational reproducibility with the need for high performance. The problem of computational reproducibility is well known in the parallel computing community. It is a requirement that the parallel code perform calculations in a fashion that will yield identical results on different configurations of processing elements on the same platform. In some cases this problem can be solved by sacrificing performance. Meeting this requirement and still achieving high performance is very difficult. Topics to be discussed include: current PSAS design and parallelization strategy; reproducibility issues; load balance vs. database memory demands, possible solutions to these problems.

Larson, J. W.↗

NASA NTRS1994

A simple hyperbolic model for communication in parallel processing environments

We introduce a model for communication costs in parallel processing environments called the 'hyperbolic model,' which generalizes two-parameter dedicated-link models in an analytically simple way. Dedicated interprocessor links parameterized by a latency and a transfer rate that are independent of load are assumed by many existing communication models; such models are unrealistic for workstation networks. The communication system is modeled as a directed communication graph in which terminal nodes represent the application processes that initiate the sending and receiving of the information and in which internal nodes, called communication blocks (CBs), reflect the layered structure of the underlying communication architecture. The direction of graph edges specifies the flow of the information carried through messages. Each CB is characterized by a two-parameter hyperbolic function of the message size that represents the service time needed for processing the message. The parameters are evaluated in the limits of very large and very small messages. Rules are given for reducing a communication graph consisting of many to an equivalent two-parameter form, while maintaining an approximation for the service time that is exact in both large and small limits. The model is validated on a dedicated Ethernet network of workstations by experiments with communication subprograms arising in scientific applications, for which a tight fit of the model predictions with actual measurements of the communication and synchronization time between end processes is demonstrated. The model is then used to evaluate the performance of two simple parallel scientific applications from partial differential equations: domain decomposition and time-parallel multigrid. In an appropriate limit, we also show the compatibility of the hyperbolic model with the recently proposed LogP model.

Stoica, Ion↗

DOE OSTI2025

Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications

In this work we introduce a novel two-level overlapping additive Schwarz preconditioner for accelerating the training of scientific machine learning applications. The design of the proposed preconditioner is motivated by the nonlinear two-level overlapping additive Schwarz preconditioner. The neural network parameters are decomposed into groups (subdomains) with overlapping regions. In addition, the network’s feed-forward structure is indirectly imposed through a novel subdomain-wise synchronization strategy and a coarse-level training step. Through a series of numerical experiments, which consider physicsinformed neural networks and operator learning approaches, we demonstrate that the proposed two-level preconditioner significantly speeds up the convergence of the standard (LBFGS) optimizer while also yielding more accurate machine learning models. Moreover, the devised preconditioner is designed to take advantage of model-parallel computations, which can further reduce the training time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Electrochemical reactivity and passivation of organic electrolytes at spinel MgCrMnO 4 cathode interfaces for rechargeable high voltage magnesium-ion batteries

Magnesium transition metal oxides such as MgCr 2−x Mn x O 4 are promising high-voltage and high-capacity cathode materials for rechargeable magnesium batteries (RMBs). Understanding and improving the chemical and electrochemical stability of the cathode–electrolyte interface (CEI) has been the primary technical emphasis to enable this category of cathode materials, which has been significantly underexplored. Herein, in this study, we focus on investigating the fundamental mechanism of parasitic reactions at the charged surface of the high-voltage MgCrMnO 4 model cathode with different organic electrolytes. The aim is to reveal the underlying effect of anions and solvents responsible for the passivation behavior of the cathode by using three exemplary anions: [(CF 3 SO 2 ) 2 N] − (TFSI − ), Al[OC(CF 3 ) 3 ] 4 − (TPFA − ), and [CB 11 H 12 ] − (MC) and three solvents: diglyme (G2), triglyme (G3), and 3-methoxypropylamine (MPA). High precision leakage current measurements during potentiostatic hold reveal that the electrolyte solvent chemistry has a more profound impact than anion's on the passivation of the MgCrMnO 4 cathode surface during deintercalation of Mg 2+ . X-ray photoelectron spectroscopy exhibits the differences in CEI composition. Amine solvents like MPA show poor passivation due to a higher degree of solvent decomposition, while the thin and anion-derived CEI in glyme-based electrolytes is directly linked with the better passivation behavior on the cathode. Furthermore, we leverage the knowledge from these findings to modify the electrolyte structure by adding a solvent additive, with the goal of reducing the parasitic reaction.

25 ENERGY STORAGE↗

NASA NTRSDate not supplied

Isotopic Tracers for CO 2 Produced During A Planetary Impact Into Limestone Target Rocks

Terrestrial meteorite impacts have been directly linked with multiple mass extinction events throughout Earth’s history. Among these impacts, those that land in sedimentary target rocks are thought to generate large quantities of CO 2 via decarbonation. This injection of CO 2 into the atmosphere has the potential to alter the climate and threaten terrestrial habitability. The magnitude of this change depends upon the net amount of CO 2 released, which is controlled by how much CO 2 is produced by the impact, how much CO 2 is removed by back-reactions after the impact, and other post-impact CO 2 sinks. To interrogate the behavior of CO 2 release into the impact atmosphere, we present carbon (δ 13 C), oxygen (δ 18 O), and clumped (Δ 47 ) isotope results from carbonate clasts preserved within the impact breccia of the Steen River Impact Structure (SRIS) in Alberta, Canada. These clasts exhibit a ~65‰ range in δ 13 C and a ~5‰ variation in δ 18 O. However, while δ 13 C and δ 18 O are positively correlated, Δ 47 unexpectedly has a negative relationship with δ 13 C and δ 18 O. Based on prior assumptions, there would either be (1) no relationship with carbonate Δ 47 and the bulk ratios because Δ 47 would be reset at extreme impact temperatures, or (2) Δ 47 would have a positive correlation with the bulk ratios, reflecting gradual Δ 47 ‘resetting.’ To reconcile the SRIS result with these expectations, we conducted a series of in vacuo heating experiments at temperatures above calcite decomposition. As predicted by Rayleigh fractionation, these heating experiments generated depletions in δ 13 C and δ 18 O that increased with reaction time. Mimicking the SRIS results, these experiments also produced concomitant increases in Δ 47 . Using an adaptation of a mechanistic model for decomposition fractionation (Hayles & Killingsworth, 2022) we hypothesize that these exotic isotope trends are caused by Rayleigh fractionation and a Δ 47 kinetic isotope effect that results from the disproportionation of O to CaO and CO 2 during thermal decomposition. This high Δ 47 CO 2 subsequently exchanges with the residual CaCO 3 . This work highlights a potential pathway for identifying and quantifying CO 2 generation by impacts and builds on the relatively limited literature characterizing the behavior of carbonate clumped isotopes at very high geologic temperatures.

Multi-isotope systems↗

DOE OSTI2025

Kernel Manifolds: Nonlinear‐Augmentation Dimensionality Reduction Using Reproducing Kernel Hilbert Spaces

This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections, augment linear dimensionality reduction with a nonlinear correction term in the reconstruction map to overcome approximation accuracy limitations of purely linear approaches. While feature map-based approaches typically learn a least squares optimal polynomial correction term, we generalize this approach by learning an optimal nonlinear correction from a user-defined reproducing kernel Hilbert space. Our approach allows one to impose arbitrary nonlinear structure on the correction term, including polynomial structure, and includes feature map and radial basis function-based corrections as special cases. Furthermore, our method has relatively low training cost and has monotonically decreasing error as the latent space dimension increases. In conclusion, we compare our approach to proper orthogonal decomposition and several recent QM approaches on data from several example problems.

kernel methods↗

DOE OSTI2025

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis

Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.

Hydrocarbons↗

DOE OSTI2025

Confocal Raman Microscopy as a Probe of Material Deconstruction in Processed Low-Density Polyethylene Particles

Confocal Raman microscopy was applied to detect structural change within individual particles of low-density polyethylene (LDPE) following chemical and electrochemical processing steps that aimed to facilitate material decomposition. A high numerical aperture (NA) oil-immersion objective enabled depth-profiling through the near surface region (20 μm–40 μm) of irregularly shaped particles with an axial spatial resolution < 2 μm estimated from measurements of instrument detection efficiency profiles. Changes in vibrational bands sensitive to polyethylene crystallinity were evident following treatments and linked to the release of low molecular weight compounds present as additives and products of processing. Effects of processing were probed by monitoring the rise of Raman scattering intensity in vibrational modes associated with polyethylene chains in a zig-zag (trans) conformation near 1128 cm –1 , 1294 cm –1 , and 1418 cm –1 , signaling chain clustering and development of organized, crystalline-like assemblies. Pristine LDPE particles displayed a uniform structure across the near surface region, while particles treated initially with chemical extractant and then further processed displayed increasingly enhanced crystallinity up to the maximum depth probed (40 μm). As a step toward measurements on ensembles of particles, least squares modeling was adapted to derive pure component spectra reflecting crystallinity change within spectral datasets. The work demonstrates high spatial resolution Raman depth-profiling for the characterization of processed polymers using a high NA immersion objective to overcome the limitations of air-objectives often used for confocal Raman microscopy.

Wahiduzzaman, Md. [Department of Chemistry and Bio↗

DOE OSTI2025

Assessment of RELAP5-3D Code with Molten Salt Heat Transfer Experiments

The accurate thermal-hydraulic assessment of thermal storage systems is crucial for addressing the integrity and performance of the storage system design. This is particularly important for thermal storage systems using molten salts as a heat transport and storage medium, as the chemical corrosion and erosion by the high-temperature molten salts can cause serious damage to the structural components of the storage system. To understand these effects for TerraPower’s Natrium® Demonstration Reactor design, the system thermal-hydraulic analysis code, RELAP5-3D, is utilized to analyze the thermal storage system. This study evaluates two heat transfer correlations implemented in the RELAP5-3D code—the Dittus-Boelter and Gnielinski correlations—using selected benchmark cases from two molten salt heat transfer experiments conducted at Xi’an Jiao Tong University and the German Aerospace Center. The Nusselt numbers calculated using the Gnielinski correlation agree with the experimental data within ±5% of the relative deviations at 300°C and 400°C. With the increasing Reynolds numbers, the Dittus-Boelter correlation underestimates the Nusselt numbers by up to 22% compared to the Gnielinski correlation. Comparison of the RELAP5-3D calculation results with experimental data at a high temperature of 550°C revealed that the thermo-chemical characteristics of solar salt at high temperatures surpassing the decomposition temperatures of nitrate salts reduce the accuracy of heat transfer model of the RELAP5-3D code. Specifically, it has been observed that the temperatures of the wall surface and liquid near the wall can surpass the decomposition temperatures, even though the bulk temperature of molten salts remains significantly lower than these decomposition temperatures. In summary, this study recommends the use of the Gnielinski correlation for the heat transfer analysis of molten salt, rather than the Dittus-Boelter correlation.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

NASA NTRS2021

Thermal Inspection of a Composite Fuselage Section Using theMethod of Proper Generalized Decomposition

Proper Generalized Decomposition (PGD) is a reduced order modeling technique for the simulation of physical systems whose governing equations depend on boundary conditions, initial conditions, material properties, and geometric parameters. It uses separated representations of system covariates combined with an iterative approximation method known as successive enrichment in order to compute an accurate parameter-dependent approximation to the full governing equations. PGD can also be used as an alternative to the Singular Value Decomposition (SVD) of a matrix and therefore as an alternative to PCA thermography. In this paper PGD was used to analyze data derived from the inspection of a composite fuselage forward section using flash thermography, and the results were compared against the standard PCA approach.

Nondestructive Evaluation↗